Manually curated dataset of catalytic peptides for ester hydrolysis.

Catalytic peptides are low cost biomolecules able to catalyse chemical reactions such as ester hydrolysis. This dataset provides a list of catalytic peptides currently reported in literature. Several parameters were evaluated, including sequence length, composition, net charge, isoelectric point, hydrophobicity, self-assembly propensity and mechanism of catalysis. Along with the analysis of physico-chemical properties, the SMILES representation for each sequence was generated to provide an easy-to-use means of training machine learning models. This offers a unique opportunity for the development and validation of proof-of-concept predictive models. Being a reliable manually curated dataset, it also enables the benchmark for comparison of new models or models trained on automatically gathered peptide-oriented datasets. Moreover, the dataset provides an insight in the currently developed catalytic mechanisms and can be used as the foundation for the development of next-generation peptide-based catalysts.

Comparing Direct Measurements and Three-Dimensional (3D) Scans for Evaluating Facial Soft Tissue.

The inspection of patients' soft tissues and the effects of various dental procedures on their facial physiognomy are quite challenging. To minimise discomfort and simplify the process of manual measuring, we performed facial scanning and computer measurement of experimentally determined demarcation lines. Images were acquired using a low-cost 3D scanner. Two consecutive scans were obtained from 39 participants, to test the scanner repeatability. An additional ten persons were scanned before and after forward movement of the mandible (predicted treatment outcome). Sensor technology that combines red, green, and blue (RGB) data with depth information (RGBD) integration was used for merging frames into a 3D object. For proper comparison, the resulting images were registered together, which was performed with ICP (Iterative Closest Point)-based techniques. Measurements on 3D images were performed using the exact distance algorithm. One operator measured the same demarcation lines directly on participants; repeatability was tested (intra-class correlations). The results showed that the 3D face scans were reproducible with high accuracy (mean difference between repeated scans <1%); the actual measurements were repeatable to some extent (excellent only for the tragus-pogonion demarcation line); computational measurements were accurate, repeatable, and comparable to the actual measurements. Three dimensional (3D) facial scans can be used as a faster, more comfortable for patients, and more accurate technique to detect and quantify changes in facial soft tissue resulting from various dental procedures.

Esterase Sequence Composition Patterns for the Identification of Catalytic Triad Microenvironment Motifs.

Ester hydrolysis is of wide biomedical interest, spanning from the green synthesis of pharmaceuticals to biomaterials' development. Existing peptide-based catalysts exhibit low catalytic efficiency compared to natural enzymes, due to the conformational heterogeneity of peptides. Moreover, there is lack of understanding of the correlation between the primary sequence and catalytic function. For this purpose, we statistically analyzed 22 EC 3.1 hydrolases with known catalytic triads, characterized by unique and well-defined mechanisms. The aim was to identify patterns at the sequence level that will better inform the creation of short peptides containing important information for catalysis, based on the catalytic triad, oxyanion holes and the triad residues microenvironments. Moreover, fragmentation schemes of the primary sequence of selected enzymes alongside the study of their amino acid frequencies, composition, and physicochemical properties are proposed. The results showed highly conserved catalytic sites with distinct positional patterns and chemical microenvironments that favor catalysis and revealed variations in catalytic site composition that could be useful for the design of minimalistic catalysts.

Predictive modeling of microbiological seawater quality in karst region using cascade model.

This paper presents an in-depth analysis of seawater quality measurements during the bathing seasons from year 2009 to 2020 in the city of Rijeka, Croatia. Due to rare occurrences of measurements with less than excellent water quality, considered dataset is deeply imbalanced. Additionally, it incorporates measurements under the influence of submerged groundwater discharges (SGD), which were observed in some bathing locations. These discharges were previously thought to dry up during the summer season and are now suspected to be one of the causes of increased Escherichia coli values. Consequently, and in view of the fact that the accuracy of prediction models can be significantly influenced by temporal and spatial variation of the input data, a novel cascade prediction modeling strategy was proposed. It consists of a sequence of prediction models which tend to identify general environmental conditions which confidently lead to excellent bathing water quality. The proposed model uses environmental features which can rather easily be estimated or obtained from the weather forecast. The model was trained on a highly biased dataset, consisting of data from locations with and without SGD influence, and for the time period spanning extremely dry and warm seasons, extremely wet seasons, as well as normal seasons. To simulate realistic application, the model was tested using temporal and spatial stratification of data. The cascade strategy was shown to be a good approach for reliably detecting environmental parameters which produce excellent water quality. Proposed model is designed as a filter method, where instances classified as less-than-excellent water quality require further analysis. The cascade model provides great flexibility as it can be customized to the particular needs of the investigated area and dataset specifics.

Sequential Properties Representation Scheme for Recurrent Neural Network-Based Prediction of Therapeutic Peptides.

The discovery of therapeutic peptides is often accelerated by means of virtual screening supported by machine learning-based predictive models. The predictive performance of such models is sensitive to the choice of data and its representation scheme. While the peptide physicochemical and compositional representations fail to distinguish sequence permutations, the amino acid arrangement within the sequence lacks the important information contained in physicochemical, conformational, topological, and geometrical properties. In this paper, we propose a solution to the identified information gap by implementing a hybrid scheme that complements the best traits from both approaches with the aim of predicting antimicrobial and antiviral activities based on experimental data from DRAMP 2.0, AVPdb, and Uniprot data repositories. Using the Friedman test of statistical significance, we compared our hybrid, sequential properties approach to peptide properties, one-hot vector encoding, and word embedding schemes in the 10-fold cross-validation setting, with respect to the F1 score, Matthews correlation coefficient, geometric mean, recall, and precision evaluation metrics. Moreover, the sequence modeling neural network was employed to gain insight into the synergic effect of both properties- and amino acid order-based predictions. The results suggest that sequential properties significantly (P < 0.01) surpasses the aforementioned state-of-the-art representation schemes. This makes it a strong candidate for increasing the predictive power of screening methods based on machine learning, applicable to any category of peptides.

Bottom-Up Design Approach for OBOC Peptide Libraries.

One-bead-one-compound peptide libraries, developed following the top-down experimental approach, have attracted great interest in the identification of potential ligands or active peptides. By exploiting a reverse experimental design approach based on the bottom-up strategy, we aimed to develop simplified, maximally diverse peptide libraries that resulted in the successful characterization of mixture components. We show that libraries of 32 and 48 components can be successfully detected in a single run using chromatography coupled to mass spectrometry (UPLC-MS). The proposed libraries were further theoretically evaluated in terms of their composition and physico-chemical properties. By combining the knowledge obtained on single libraries we can cover larger sequence spaces and provide a controlled exploration of the peptide chemical space both theoretically and experimentally. Designing libraries by using the bottom-up approach opens up the possibility of rationally fine-tuning the library complexity based on the available analytical methods.

Algorithm-supported, mass and sequence diversity-oriented random peptide library design.

Random peptide libraries that cover large search spaces are often used for the discovery of new binders, even when the target is unknown. To ensure an accurate population representation, there is a tendency to use large libraries. However, parameters such as the synthesis scale, the number of library members, the sequence deconvolution and peptide structure elucidation, are challenging when increasing the library size. To tackle these challenges, we propose an algorithm-supported approach to peptide library design based on molecular mass and amino acid diversity. The aim is to simplify the tedious permutation identification in complex mixtures, when mass spectrometry is used, by avoiding mass redundancy. For this purpose, we applied multi (two- and three-)-objective genetic algorithms to discriminate between library members based on defined parameters. The optimizations led to diverse random libraries by maximizing the number of amino acid permutations and minimizing the mass and/or sequence overlapping. The algorithm-suggested designs offer to the user a choice of appropriate compromise solutions depending on the experimental needs. This implies that diversity rather than library size is the key element when designing peptide libraries for the discovery of potential novel biologically active peptides.