Search Results (74)

Background: Peptides are a class of molecules that can be presented as good antimicrobials and with mechanisms that avoid resistance, and the design of peptides with good activity can be complex and laborious. The study of their quantitative structure–activity relationships through machine learning algorithms can shed light on a rational and effective design. Methods: Information on the antimicrobial activity of peptides was collected, and their structures were characterized by molecular descriptors generation to design regression and classification models based on machine learning algorithms. The contribution of each descriptor in the generated models was evaluated by determining its relative importance and, finally, the antimicrobial activity of new peptides was estimated. Results: A structured database of antimicrobial peptides and their descriptors was obtained, with which 56 machine learning models were generated. Random Forest-based models showed better performance, and of these, regression models showed variable performance (R² = 0.339–0.574), while classification models showed good performance (MCC = 0.662–0.755 and ACC = 0.831–0.877). Those models based on bacterial groups showed better performance than those based on the entire dataset. The properties of the new peptides generated are related to important descriptors that encode physicochemical properties such as lower molecular weight, higher charge, propensity to form alpha-helical structures, lower hydrophobicity, and higher frequency of amino acids such as lysine and serine. Conclusions: Machine learning models allowed to establish the structure–activity relationships of antimicrobial peptides. Classification models performed better than regression models. These models allowed us to make predictions and new peptides with high antimicrobial potential were proposed. Full article

Hepatitis C virus (HCV) infection remains a significant global health burden, driven by the emergence of drug-resistant strains and the limited efficacy of current antiviral therapies. A promising strategy for therapeutic intervention involves targeting the NS3 protease, a viral enzyme essential for replication. In this study, we present the first computational model specifically designed to identify NS3 protease inhibitory peptides (NS3IPs). Using amino acid composition (AAC) and K-spaced amino acid pair composition (CKSAAP) features, we developed machine learning classifiers based on support vector machine (SVM) and random forest (RF), achieving accuracies of 98.85% and 97.83%, respectively, validated through 5-fold cross-validation and independent testing. To support the accessibility of the strategy, we implemented a web-based tool, iDNS3IP, which enables real-time prediction of NS3IPs. In addition, we performed feature space analyses using PCA, t-SNE, and LDA based on AAindex descriptors. The resulting visualizations showed a distinguishable clustering between NS3IPs and non-inhibitory peptides, suggesting that inhibitory activity may correlate with characteristic physicochemical patterns. This study provides a reliable and interpretable platform to assist in the discovery of therapeutic peptides and supports continued research into peptide-based antiviral strategies for drug-resistant HCV. To enhance its flexibility, the iDNS3IP web tool also incorporates a BLAST-based similarity search function, enabling users to evaluate inhibitory candidates from both predictive and homology-based perspectives. Full article

This study explores the production and valorization of hemp seed cake protein concentrate (HPC) as a functional ingredient to enhance the nutritional quality and sensory attributes of choux pastry products, specifically éclairs. By integrating varied concentrations of HPC (0%, 1%, 5%, 10%, 15%, and 20%) into traditional formulations, the physicochemical properties, proximate composition, amino acid profile, and sensory characteristics of the resulting pastries were assessed. Sensory attributes were assessed using the check-all-that-apply (CATA) method, where a trained panel selected applicable descriptors from a predefined list. Results indicated that the incorporation of HPC significantly increased protein content from 8.23% in the control sample (HPC0%) to 11.32% in the HPC20% formulation and improved moisture retention, leading to greater exterior and interior éclairs volume, increasing from 42.15 cm³ to 51.5 cm³ and from 18.34 cm³ to 38.47 cm³, respectively. Furthermore, sensory evaluation revealed pronounced differences in attributes such as flavor, appearance, and mouthfeel, with optimal sensory profiles noted at 10% HPC inclusion. The amino acid analysis demonstrated a balanced composition, particularly of essential amino acids, emphasizing HPC’s potential as a valuable protein source, with significant contributions from leucine (8.17 g/100 g protein), isoleucine (5.56 g/100 g protein), and phenylalanine (6.31 g/100 g protein), as well as notable levels of immunoactive amino acids such as arginine (10.92 g/100 g protein) and glutamic acid (20.16 g/100 g protein). These findings highlight the significant nutritional benefits of HPC enrichment, supporting the development of healthier bakery products and contributing to sustainable food practices within the industry. Full article

Most life activities of organisms are realized through protein–protein interactions, and these interactions are mainly achieved through residue–residue contact between monomer proteins. Consequently, studying residue–residue contact at the protein interaction interface can contribute to a deeper understanding of the protein–protein interaction mechanism. In this paper, we focus on the research of the trimer protein interface residue pair. Firstly, we utilize the amino acid k-interval product factor descriptor ($\mathrm{A}\mathrm{A}\mathrm{I}\mathrm{P}\mathrm{F}\left(\mathrm{k}\right)$) to integrate the positional information and physicochemical properties of amino acids, combined with the electric properties and geometric shape features of residues, to construct an 8 × 16 multi-feature map. This multi-feature map represents a sample composed of two residues on a trimer protein. Secondly, we construct a CNN-GRU deep learning framework to predict the trimer protein interface residue pair. The results show that when each dimer protein provides 10 prediction results and two protein–protein interaction interfaces of a trimer protein needed to be accurately predicted, the accuracy of our proposed method is 60%. When each dimer protein provides 10 prediction results and one protein–protein interaction interface of a trimer protein needs to be accurately predicted, the accuracy of our proposed method is 93%. Our results can provide experimental researchers with a limited yet precise dataset containing correct trimer protein interface residue pairs, which is of great significance in guiding the experimental resolution of the trimer protein three-dimensional structure. Furthermore, compared to other computational methods, our proposed approach exhibits superior performance in predicting residue–residue contact at the trimer protein interface. Full article

Protein kinases and phosphatases are key signaling proteins and are important drug targets. An explosion in the number of publicly available 3D structures of proteins has been seen in recent years. Three-dimensional structures of kinase and phosphatase have not been systematically investigated. This is due to the difficulty of designing structure-based descriptors that are capable of quantifying conformational changes. We have developed a triangular spatial relationship (TSR)-based algorithm that enables a unique representation of a protein’s 3D structure using a vector of integers (keys). The main objective of this study is to provide structural insight into conformational changes. We also aim to link TSR-based structural descriptors to their functions. The 3D structures of 2527 kinases and 505 phosphatases are studied. This study results in several major findings as follows: (i) The clustering method yields functionally coherent clusters of kinase and phosphatase families and their superfamilies. (ii) Specific TSR keys are identified as structural signatures for different types of kinases and phosphatases. (iii) TSR keys can identify different conformations of the well-known DFG motif of kinases. (iv) A significant number of phosphatases have their own distinct DFG motifs. The TSR keys from kinases and phosphatases agree with each other. TSR keys are successfully used to represent and quantify conformational changes of CDK2 upon the binding of cyclin or phosphorylation. TSR keys are effective when used as features for unsupervised machine learning and for key searches. If discriminative TSR keys are identified, they can be mapped back to atomic details within the amino acids involved. In conclusion, this study presents an advanced computational methodology with significant advantages in not only representing and quantifying conformational changes of protein structures but also having the capability of directly linking protein structures to their functions. Full article

Food-derived peptides are usually safe natural drug candidates that can potentially inhibit the angiotensin-converting enzyme (ACE). The wet experiments used to identify ACE inhibitory peptides (ACEiPs) are time-consuming and costly, making it important and urgent to reduce the scope of experimental validation through bioinformatics methods. Here, we construct an ACE inhibitory peptide predictor (ACEiPP) using optimized amino acid descriptors (AADs) and long- and short-term memory neural networks. Our results show that combined-AAD models exhibit more efficient feature transformation ability than single-AAD models, especially the training model with the optimal descriptors as the feature inputs, which exhibits the highest predictive ability in the independent test (Acc = 0.9479 and AUC = 0.9876), with a significant performance improvement compared to the existing three predictors. The model can effectively characterize the structure–activity relationship of ACEiPs. By combining the model with database mining, we used ACEiPP to screen four ACEiPs with multiple reported functions. We also used ACEiPP to predict peptides from 21,249 food-derived proteins in the Database of Food-derived Bioactive Peptides (DFBP) and construct a library of potential ACEiPs to facilitate the discovery of new anti-ACE peptides. Full article

Quinoa is a healthy food that possesses high levels of protein that is enriched for dietary essential amino acids. The crop is highly diverse and well-adapted to changing climatic conditions. In spite of being vulnerable to pests and diseases, the development of new resistant varieties is possible. Taking advantage of this genetic variability is crucial for breeding programs, especially to adapt quinoa to the shifting needs of producers. In this study, 25 Peruvian accessions and two commercial varieties were characterized and agronomically evaluated in the Peruvian Pacific desert. Specific methodologies and descriptors of existing crops were used, analyzing a total of 24 quantitative and 23 qualitative variables with 15 repetitions per accession. The data were processed using descriptive statistics and a multivariate analysis. The results showed a high variability in morphological characteristics, with an area under the disease progress curve (AUDPC) of the presence of mildew between 529 and 1725, highlighting ACC06 with a lower severity of mildew. The percentage of saponins varied between 0.04 and 0.21 percent, with ACC06 being the one with the lowest percentage. Regarding the crop yield, it ranged between 0.35 and 8.80 t ha⁻¹, highlighting the high-yielding accessions ACC55 and ACC14. These results were promising for the improvement of quinoa yield in the production conditions of the Peruvian Pacific desert. Full article

In this paper, an alignment-free bioinformatics technique, termed the 20D-Dynamic Representation of Protein Sequences, is utilized to investigate the similarity/dissimilarity between Baculovirus and Echinococcus multilocularis genome sequences. In this method, amino acid sequences are depicted as 20D-dynamic graphs, comprising sets of “material points” in a 20-dimensional space. The spatial distribution of these material points is indicative of the sequence characteristics and is quantitatively described by sequence descriptors akin to those employed in dynamics, such as coordinates of the center of mass of the 20D-dynamic graph and the tensor of the moment of inertia of the graph (defined as a symmetric matrix). Each descriptor unveils distinct features of similarity and is employed to establish similarity relations among the examined sequences, manifested either as a symmetric distance matrix (“similarity matrix”), a classification map, or a phylogenetic tree. The classification maps are introduced as a new way of visualizing the similarity relations obtained using the 20D-Dynamic Representation of Protein Sequences. Some classification maps are obtained using the Principal Component Analysis (PCA) for the center of mass coordinates and normalized moments of inertia of 20D-dynamic graphs as input data. Although the method operates in a multidimensional space, we also apply some visualization techniques, including the projection of 20D-dynamic graphs onto a 2D plane. Studies on model sequences indicate that the method is of high quality, both graphically and numerically. Despite the high similarity observed among the sequences of E. multilocularis, subtle discrepancies can be discerned on the 2D graphs. Employing this approach has led to the discovery of numerous new similarity relations compared to our prior study conducted at the DNA level, using the 4D-Dynamic Representation of DNA/RNA Sequences, another alignment-free bioinformatics method also introduced by us. Full article

Fungal resistance is a public health concern due to the limited availability of antifungal resources and the complexities associated with treating persistent fungal infections. Azoles are thus far the primary line of defense against fungi. Specifically, azoles inhibit the conversion of lanosterol to ergosterol, producing defective sterols and impairing fluidity in fungal plasmatic membranes. Studies on azole resistance have emphasized specific point mutations in CYP51/ERG11 proteins linked to resistance. Although very insightful, the traditional approach to studying azole resistance is time-consuming and prone to errors during meticulous alignment evaluation. It relies on a reference-based method using a specific protein sequence obtained from a wild-type (WT) phenotype. Therefore, this study introduces a machine learning (ML)-based approach utilizing molecular descriptors representing the physiochemical attributes of CYP51/ERG11 protein isoforms. This approach aims to unravel hidden patterns associated with azole resistance. The results highlight that descriptors related to amino acid composition and their combination of hydrophobicity and hydrophilicity effectively explain the slight differences between the resistant non-wild-type (NWT) and WT (nonresistant) protein sequences. This study underscores the potential of ML to unravel nuanced patterns in CYP51/ERG11 sequences, providing valuable molecular signatures that could inform future endeavors in drug development and computational screening of resistant and nonresistant fungal lineages. Full article

The sea bottom acts as a key natural archive where the memory of long-term timescale environmental changes is recorded. This study discusses some ecological and chemical features of fjord sediments that were explored during the AREX cruise carried out in the Svalbard archipelago in the summer of 2021. The activity rates of the enzymes leucine aminopeptidase (LAP), beta-glucosidase (GLU), and alkaline phosphatase (AP) and community-level physiological profiles (CLPPs) were studied with the aim of determining the functional diversity of the benthic microbial community, while bacterial isolates were screened for their susceptibility to antibiotics in order to explore the role of these extreme environments as potential reservoirs of antibiotic resistance. Enzyme activity rates were obtained using fluorogenic substrates, and CLPPs were obtained using Biolog Ecoplates; antibiotic susceptibility assays were performed through the standard disk diffusion method. Spatial trends observed in the functional profiles of the microbial community suggested variability in the microbial community’s composition, presumably related to the patchy distribution of organic substrates. Complex carbon sources, carbohydrates, and amino acids were the organic polymers preferentially metabolized by the microbial community. Multi-resistance to enrofloxacin and tetracycline was detected in all of the examined samples, stressing the role of sediments as a potential reservoir of chemical wastes ascribable to antibiotic residuals. This study provides new insights on the health status of fjord sediments of West Spitsbergen, applying a dual ecological and biochemical approach. Microbial communities in the fjord sediments showed globally a good functional diversity, suggesting their versatility to rapidly react to changing conditions. The lack of significant diversification among the three studied areas suggests that microbial variables alone cannot be suitable descriptors of sediment health, and that additional measures (i.e., physical–chemical characteristics) should be taken to better define environmental status. Full article

Iontophoresis enables the non-invasive transdermal delivery of moderately-sized proteins and the needle-free cutaneous delivery of antibodies. However, simple descriptors of protein characteristics cannot accurately predict the feasibility of iontophoretic transport. This study investigated the cathodal and anodal iontophoretic transport of the negatively charged M7D12H nanobody and a series of negatively charged variants with single amino acid substitutions. Surprisingly, M7D12H and its variants were only delivered transdermally by anodal iontophoresis. In contrast, transdermal permeation after cathodal iontophoresis and passive diffusion was <LOQ. The anodal iontophoretic delivery of these negatively charged proteins was achieved because electroosmosis was the dominant electrotransport mechanism. Cutaneous deposition after the anodal iontophoresis of M7D12H_WT (wild type), and the R54E and K65E variants, was statistically superior to that after cathodal iontophoresis (6.07 ± 2.11, 9.22 ± 0.80, and 14.45 ± 3.45 μg/cm², versus 1.12 ± 0.30, 0.72 ± 0.27, and 0.46 ± 0.07 µg/cm², respectively). This was not the case for S102E, where cutaneous deposition after anodal and cathodal iontophoresis was 11.89 ± 0.87 and 8.33 ± 2.62 µg/cm², respectively; thus, a single amino acid substitution appeared to be sufficient to impact the iontophoretic transport of a 17.5 kDa protein. Visualization studies using immunofluorescent labeling showed that skin transport of M7D12H_WT was achieved via the intercellular and follicular routes. Full article

The interaction of a series of metal derivatives of 5, 10, 15, 20-tetrakis(4-sulfonato-phenyl)porphyrin (MTPPS₄, M = Cu(II), Pt(II), Ni(II), Zn(II) and Co(II)), including the metal free porphyrin (TPPS₄), with the aromatic amino acids L-tryptophan (L-Trp), L-and D-phenylalanine (L-and D-Phe) and L-histidine (L-His) have been investigated through UV/Vis spectroscopy. The amino acid L-serine (L-Ser) has been included as reference compound. The spectroscopic changes induced by adding the amino acids have been exploited to evaluate the extent of interaction between the molecular components in the supramolecular adducts. The binding constants have been estimated for most of the investigated systems, assuming a simple 1:1 equilibrium. The bathochromic shifts of the B-bands, the extent of hypochromicity and the binding constants have been analyzed through two chemical descriptors. All the data point to the important role played by the steric hindrance introduced by axial ligands coordinated to the metal ions and to the degree of hydrophobicity and size of the aromatic moiety in the amino acids. Full article

The need for new antimicrobial agents (AntAg) is driven by the persistent antibiotic resistance in microorganisms, as well as the increasing frequency of pandemics. Due to the deficiency of AntAg, research aimed at developing speedy approaches to find new drug candidates is relevant. This study aims to conduct an in silico study of the biological activity spectrum as well as the molecular binding mechanisms of four structurally different forms of levofloxacin (Lvf) with bacterial topoisomerases targets of type IIA (DNA gyrase and topoisomerase IV) to enable the development of drugs with an improved characterization of the safety profile. To achieve this goal, a number of software products were used, such as ChemicPen v. 2.6, PyMol 2.5, Avogadro 1.2.0, PASS, AutoDockTools 1.5.7 with the new generation software Autodock Vina. These software products are the first to be made available for visualization of clusters with determination of ligand-receptor pair binding affinity, as well as clustering coordinates and proposed mechanisms of action. One of the real structures of Lvf, a decarboxylated derivative, was obtained with tribochemical (TrbCh) exposure. The action spectrum of molecular ligands is described based on a Bayesian probability activity prediction model (PASS software Version 2.0). Predicted and real (PMS and RMS) molecular structures of Lvf, with decreasing levels of structural complexity, were translated into descriptors via Wiener (W), Balaban (Vs), Detour (Ip), and Electropy € indices. The 2D «structure-activity» diagrams were used to differentiate closely related structures of levofloxacin. PMS and RMS were visualized as 3D models of the ligand-receptor complexes. The contact regions of RMS and PMS with key amino acid residues—SER-79, DT-15, DG-1, DA-1—were demonstrated. The intra- and inter-molecular binding sites, data on free energy (affinity values, kcal/mol), the binding constant K_b (M⁻¹), and the number of clusters are presented. The research results obtained from the presented in silico approach to explore the spectrum of action find quantitative “structure-activity” correlations, and predict molecular mechanisms may be of applied interest for directed drug discovery. Full article

This study aimed to analyze the structural requirements for di- and tripeptides exhibiting a DPP IV-inhibitory effect. The sequences of 46 di- and 33 tripeptides, including their bioactivity (IC₅₀; μM), were implemented from the BIOPEP-UWM database, whereas modeling was performed using SCIGRESS Explorer: Version FJ 3.5.1 software. Models included 336 (dipeptide dataset) and 184 descriptors (tripeptide dataset). The values of the determination coefficient (R²) defining model reliability were 0.782 and 0.829 for di- and tripeptides, respectively. Based on the implemented descriptors, it was concluded that increased numbers of nitrogen atoms, as well as the methyl groups, are required for dipeptides to enhance the DPP IV-inhibitory effect. This was indicated by the presence of amino acids with an aliphatic side chain (e.g., Leu, Val, Ile) and an aromatic ring (Trp). In the case of tripeptides, a correlation was found between their molecular weight (MW) and studied bioactivity. A tripeptide with a molecular weight of up to 500 Da was found suitable for the sequence to act as the DPP IV inhibitor. Although there is still a gap in explaining the relations between the structural nature and the DPP IV-inhibitory activity of peptides, and certain issues related to this topic still remain unknown, the results are in line with those reported by other authors. Additionally, the suitability of the SCIGRESS tool in the QSAR analysis of peptides derived from foods can be confirmed. Interpretable descriptors enabled the achievement of more unequivocal results concerning the main structural factors affecting the DPP IV inhibition of di- and tripeptides. Full article

The folded structures of proteins can be accurately predicted by deep learning algorithms from their amino-acid sequences. By contrast, in spite of decades of research studies, the prediction of folding pathways and the unfolded and misfolded states of proteins, which are intimately related to diseases, remains challenging. A two-state (folded/unfolded) description of protein folding dynamics hides the complexity of the unfolded and misfolded microstates. Here, we focus on the development of simplified order parameters to decipher the complexity of disordered protein structures. First, we show that any connected, undirected, and simple graph can be associated with a linear chain of atoms in thermal equilibrium. This analogy provides an interpretation of the usual topological descriptors of a graph, namely the Kirchhoff index and Randić resistance, in terms of effective force constants of a linear chain. We derive an exact relation between the Kirchhoff index and the average shortest path length for a linear graph and define the free energies of a graph using an Einstein model. Second, we represent the three-dimensional protein structures by connected, undirected, and simple graphs. As a proof of concept, we compute the topological descriptors and the graph free energies for an all-atom molecular dynamics trajectory of folding/unfolding events of the proteins Trp-cage and HP-36 and for the ensemble of experimental NMR models of Trp-cage. The present work shows that the local, nonlocal, and global force constants and free energies of a graph are promising tools to quantify unfolded/disordered protein states and folding/unfolding dynamics. In particular, they allow the detection of transient misfolded rigid states. Full article