The resonant recognition model (RRM) predicts amino acid residues in highly conserved regions of the hormone prolactin (PRL)

A comprehensive framework for advanced protein classification and function prediction using synergistic approaches: Integrating bispectral analysis, machine learning, and deep learning

Proteins are fundamental components of diverse cellular systems and play crucial roles in a variety of disease processes. Consequently, it is crucial to comprehend their structure, function, and intricate interconnections. Classifying proteins into families or groups with comparable structural and functional characteristics is a crucial aspect of this comprehension. This classification is crucial for evolutionary research, predicting protein function, and identifying potential therapeutic targets. Sequence alignment and structure-based alignment are frequently ineffective techniques for identifying protein families.This study addresses the need for a more efficient and accurate technique for feature extraction and protein classification. The research proposes a novel method that integrates bispectrum characteristics, deep learning techniques, and machine learning algorithms to overcome the limitations of conventional methods. The proposed method uses numbers to represent protein sequences, utilizes bispectrum analysis, uses different topologies for convolutional neural networks to pull out features, and chooses robust features to classify protein families. The goal is to outperform existing methods for identifying protein families, thereby enhancing classification metrics. The materials consist of numerous protein datasets, whereas the methods incorporate bispectrum characteristics and deep learning strategies. The results of this study demonstrate that the proposed method for identifying protein families is superior to conventional approaches. Significantly enhanced quality metrics demonstrated the efficacy of the combined bispectrum and deep learning approaches. These findings have the potential to advance the field of protein biology and facilitate pharmaceutical innovation. In conclusion, this study presents a novel method that employs bispectrum characteristics and deep learning techniques to improve the precision and efficiency of protein family identification. The demonstrated advancements in classification metrics demonstrate this method's applicability to numerous scientific disciplines. This furthers our understanding of protein function and its implications for disease and treatment.

Chemical Composition of Pigeon Crop Milk and Factors Affecting Its Production: A Review

Pigeons are important commercial poultry in addition to being ornamental birds. In 2021, more than 111 million pairs of breeding pigeons were kept in stock and 1.6 billion squabs were slaughtered for meat in China. However, in many countries, pigeons are not domestic birds; thus, it is necessary to elucidate the factors involved in their growth and feeding strategy due to their economic importance. Pigeons are altricial birds, so feedstuffs cannot be digested by squabs, which instead are fed a mediator named pigeon crop milk. During lactation, breeding pigeons (both female and male) ingest diets and generate crop milk to feed squabs. Thus, research on squab growth is more complex than that on chicken and other poultry. To date, research on the measurement of crop milk composition and estimation of the factors affecting its production has not ceased, and these results are worth reviewing to guide production. Moreover, some studies have focused on the formation mechanism of crop milk, reporting that the synthesis of crop milk is controlled by prolactin and insulin-activated pathways. Furthermore, the Janus kinase 2 (JAK2)-signal transducer and activator of transcription 5 (STAT5) pathway, target of rapamycin (TOR) pathway and AMP-activated protein kinase (AMPK) pathway were also reported to be involved in crop milk synthesis. Therefore, this review focuses on the chemical composition of pigeon crop milk and factors affecting its production during lactation. This work explores novel mechanisms and provides a theoretical reference for improving production in the pigeon industry, including for racing, ornamental purposes, and production of meat products.

Generalized Property-Based Encoders and Digital Signal Processing Facilitate Predictive Tasks in Protein Engineering

Computational methods in protein engineering often require encoding amino acid sequences, i.e., converting them into numeric arrays. Physicochemical properties are a typical choice to define encoders, where we replace each amino acid by its value for a given property. However, what property (or group thereof) is best for a given predictive task remains an open problem. In this work, we generalize property-based encoding strategies to maximize the performance of predictive models in protein engineering. First, combining text mining and unsupervised learning, we partitioned the AAIndex database into eight semantically-consistent groups of properties. We then applied a non-linear PCA within each group to define a single encoder to represent it. Then, in several case studies, we assess the performance of predictive models for protein and peptide function, folding, and biological activity, trained using the proposed encoders and classical methods (One Hot Encoder and TAPE embeddings). Models trained on datasets encoded with our encoders and converted to signals through the Fast Fourier Transform (FFT) increased their precision and reduced their overfitting substantially, outperforming classical approaches in most cases. Finally, we propose a preliminary methodology to create de novo sequences with desired properties. All these results offer simple ways to increase the performance of general and complex predictive tasks in protein engineering without increasing their complexity.

A machine learning approach for reliable prediction of amino acid interactions and its application in the directed evolution of enantioselective enzymes

Directed evolution is an important research activity in synthetic biology and biotechnology. Numerous reports describe the application of tedious mutation/screening cycles for the improvement of proteins. Recently, knowledge-based approaches have facilitated the prediction of protein properties and the identification of improved mutants. However, epistatic phenomena constitute an obstacle which can impair the predictions in protein engineering. We present an innovative sequence-activity relationship (innov'SAR) methodology based on digital signal processing combining wet-lab experimentation and computational protein design. In our machine learning approach, a predictive model is developed to find the resulting property of the protein when the n single point mutations are permuted (2n combinations). The originality of our approach is that only sequence information and the fitness of mutants measured in the wet-lab are needed to build models. We illustrate the application of the approach in the case of improving the enantioselectivity of an epoxide hydrolase from Aspergillus niger. n = 9 single point mutants of the enzyme were experimentally assessed for their enantioselectivity and used as a learning dataset to build a model. Based on combinations of the 9 single point mutations (29), the enantioselectivity of these 512 variants were predicted, and candidates were experimentally checked: better mutants with higher enantioselectivity were indeed found.

Application of fourier transform and proteochemometrics principles to protein engineering

Background

Connecting the dots between the protein sequence and its function is of fundamental interest for protein engineers. In-silico methods are useful in this quest especially when structural information is not available. In this study we propose a mutant library screening tool called iSAR (innovative Sequence Activity Relationship) that relies on the physicochemical properties of the amino acids, digital signal processing and partial least squares regression to uncover these sequence-function correlations.

Results

We show that the digitalized representation of the protein sequence in the form of a Fourier spectrum can be used as an efficient descriptor to model the sequence-activity relationship of proteins. The iSAR methodology that we have developed identifies high fitness mutants from mutant libraries relying on physicochemical properties of the amino acids, digital signal processing and regression techniques. iSAR correlates variations caused by mutations in spectra with biological activity/fitness. It takes into account the impact of mutations on the whole spectrum and does not focus on local fitness alone. The utility of the method is illustrated on 4 datasets: cytochrome P450 for thermostability, TNF-alpha for binding affinity, GLP-2 for potency and enterotoxins for thermostability. The choice of the datasets has been made such as to illustrate the ability of the method to perform when limited training data is available and also when novel mutations appear in the test set, that have not been featured in the training set.

Conclusion

The combination of Fast Fourier Transform and Partial Least Squares regression is efficient in capturing the effects of mutations on the function of the protein. iSAR is a fast algorithm which can be implemented with limited computational resources and can make effective predictions even if the training set is limited in size.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2407-8) contains supplementary material, which is available to authorized users.

A Complex Prime Numerical Representation of Amino Acids for Protein Function Comparison

Computationally assessing the functional similarity between proteins is an important task of bioinformatics research. It can help molecular biologists transfer knowledge on certain proteins to others and hence reduce the amount of tedious and costly benchwork. Representation of amino acids, the building blocks of proteins, plays an important role in achieving this goal. Compared with symbolic representation, representing amino acids numerically can expand our ability to analyze proteins, including comparing the functional similarity of them. Among the state-of-the-art methods, electro-ion interaction pseudopotential (EIIP) is widely adopted for the numerical representation of amino acids. However, it could suffer from degeneracy that two different amino acid sequences have the same numerical representation, due to the design of EIIP. In light of this challenge, we propose a complex prime numerical representation (CPNR) of amino acids, inspired by the similarity between a pattern among prime numbers and the number of codons of amino acids. To empirically assess the effectiveness of the proposed method, we compare CPNR against EIIP. Experimental results demonstrate that the proposed method CPNR always achieves better performance than EIIP. We also develop a framework to combine the advantages of CPNR and EIIP, which enables us to improve the performance and study the unique characteristics of different representations.

When 1+1>2: Nanostructured composites for hard tissue engineering applications

Multicomponent, synergistic and multifunctional nanostructures have taken over the spotlight in the realm of biomedical nanotechnologies. The most prospective materials for bone regeneration today are almost exclusively composites comprising two or more components that compensate for the shortcomings of each one of them alone. This is quite natural in view of the fact that all hard tissues in the human body, except perhaps the tooth enamel, are composite nanostructures. This review article highlights some of the most prospective breakthroughs made in this research direction, with the hard tissues in main focus being those comprising bone, tooth cementum, dentin and enamel. The major obstacles to creating collagen/apatite composites modeled after the structure of bone are mentioned, including the immunogenicity of xenogeneic collagen and continuously failing attempts to replicate the biomineralization process in vitro. Composites comprising a polymeric component and calcium phosphate are discussed in light of their ability to emulate the soft/hard composite structure of bone. Hard tissue engineering composites created using hard material components other than calcium phosphates, including silica, metals and several types of nanotubes, are also discoursed on, alongside additional components deliverable using these materials, such as cells, growth factors, peptides, antibiotics, antiresorptive and anabolic agents, pharmacokinetic conjugates and various cell-specific targeting moieties. It is concluded that a variety of hard tissue structures in the body necessitates a similar variety of biomaterials for their regeneration. The ongoing development of nanocomposites for bone restoration will result in smart, theranostic materials, capable of acting therapeutically in direct feedback with the outcome of in situ disease monitoring at the cellular and subcellular scales. Progress in this research direction is expected to take us to the next generation of biomaterials, designed with the purpose of fulfilling Daedalus' dream - not restoring the tissues, but rather augmenting them.

CISAPS: Complex Informational Spectrum for the Analysis of Protein Sequences

Complex informational spectrum analysis for protein sequences (CISAPS) and its web-based server are developed and presented. As recent studies show, only the use of the absolute spectrum in the analysis of protein sequences using the informational spectrum analysis is proven to be insufficient. Therefore, CISAPS is developed to consider and provide results in three forms including absolute, real, and imaginary spectrum. Biologically related features to the analysis of influenza A subtypes as presented as a case study in this study can also appear individually either in the real or imaginary spectrum. As the results presented, protein classes can present similarities or differences according to the features extracted from CISAPS web server. These associations are probable to be related with the protein feature that the specific amino acid index represents. In addition, various technical issues such as zero-padding and windowing that may affect the analysis are also addressed. CISAPS uses an expanded list of 611 unique amino acid indices where each one represents a different property to perform the analysis. This web-based server enables researchers with little knowledge of signal processing methods to apply and include complex informational spectrum analysis to their work.

Amelogenin in Enamel Tissue Engineering

In this chapter the basic premises, the recent findings and the future challenges in the use of amelogenin for enamel tissue engineering are being discoursed on. Results emerging from the experiments performed to assess the fundamental physicochemical mechanisms of the interaction of amelogenin, the main protein of the enamel matrix, and the growing crystals of apatite, are mentioned, alongside a moderately comprehensive literature review of the subject at hand. The clinical importance of understanding this protein/mineral interaction at the nanoscale are highlighted as well as the potential for tooth enamel to act as an excellent model system for studying some of the essential aspects of biomineralization processes in general. The dominant paradigm stating that amelogenin directs the uniaxial growth of apatite crystals in enamel by slowing down the growth of (hk0) faces on which it adheres is being questioned based on the results demonstrating the ability of amelogenin to promote the nucleation and crystal growth of apatite under constant titration conditions designed to mimic those present in the developing enamel matrix. The role of numerous minor components of the enamel matrix is being highlighted as essential and impossible to compensate for by utilizing its more abundant ingredients only. It is concluded that the three major aspects of amelogenesis outlined hereby--(1) the assembly of amelogenin and other enamel matrix proteins, (2) the proteolytic activity, and (3) crystallization--need to be in precise synergy with each other in order for the grounds for the proper imitation of amelogenesis in the lab to be created.

Biological effects of a de novo designed myxoma virus peptide analogue: evaluation of cytotoxicity on tumor cells

Background

The Resonant Recognition Model (RRM) is a physico-mathematical model that interprets protein sequence linear information using digital signal processing methods. In this study the RRM concept was employed for structure-function analysis of myxoma virus (MV) proteins and the design of a short bioactive therapeutic peptide with MV-like antitumor/cytotoxic activity.

Methodology/Principal Findings

The analogue RRM-MV was designed by RRM as a linear 18 aa 2.3 kDa peptide. The biological activity of this computationally designed peptide analogue against cancer and normal cell lines was investigated. The cellular cytotoxicity effects were confirmed by confocal immunofluorescence microscopy, by measuring the levels of cytoplasmic lactate dehydrogenase (LDH) and by Prestoblue cell viability assay for up to 72 hours in peptide treated and non-treated cell cultures. Our results revealed that RRM-MV induced a significant dose and time-dependent cytotoxic effect on murine and human cancer cell lines. Yet, when normal murine cell lines were similarly treated with RRM-MV, no cytotoxic effects were observed. Furthermore, the non-bioactive RRM designed peptide RRM-C produced negligible cytotoxic effects on these cancer and normal cell lines when used at similar concentrations. The presence/absence of phosphorylated Akt activity in B16F0 mouse melanoma cells was assessed to indicate the possible apoptosis signalling pathway that could be affected by the peptide treatment. So far, Akt activity did not seem to be significantly affected by RRM-MV as is the case for the original viral protein.

Conclusions/Significance

Our findings indicate the successful application of the RRM concept to design a bioactive peptide analogue (RRM-MV) with cytotoxic effects on tumor cells only. This 2.345 kDa peptide analogue to a 49 kDa viral protein may be suitable to be developed as a potential cancer therapeutic. These results also open a new direction to the rational design of therapeutic agents for future cancer treatment.

Prospects and Pits on the Path of Biomimetics: The case of tooth enamel

This review presents a discourse on challenges in understanding and imitating the process of amelogenesis in vitro on the molecular scale. In light of the analysis of imitation of the growth of dental enamel, it also impends on the prospects and potential drawbacks of the biomimetic approach in general. As the formation of enamel proceeds with the protein matrix guiding the crystal growth, while at the same time conducting its own degradation and removal, it is argued that three aspects of amelogenesis need to be induced in parallel: a) crystal growth; b) protein assembly; c) proteolytic degradation. A particular emphasis is therefore placed on ensuring conditions for proteolysis-coupled protein-guided crystallization to occur. Discussed are structural and functional properties of the protein species involved in amelogenesis, mainly amelogenin and enamelysin, the main protein and the protease of the developing enamel matrix, respectively. A model of enamel growth based on controlled delivery of constituent ions or crystalline or amorphous building blocks by means of amelogenin is proposed. The importance of high viscosity of the enamel matrix and a more intricate role that water may play in such a gelatinous medium are also touched upon. The tendency of amelogenin to self-assemble into fibrous and rod-shaped morphologies is considered as potentially important in explaining the formation of elongated apatite crystals. The idea that a preassembling protein matrix serves as a template for the uniaxial growth of apatite crystals in enamel is finally challenged with the one based on co-assembly of the protein and the mineral phases.

Ataxin active site determination using spectral distribution of electron ion interaction potentials of amino acids

Ataxia is a genetic neurological disorder characterised by a neurodegenerative process affecting a motor cortex responsible for balance and coordination. Recently several genes that cause autosomal dominant ataxia development were identified. These abnormal genes share a common ability to produce abnormal ataxin proteins that can affect nerve cells in the cerebellum and spinal cord. Here, using the Resonant Recognition Model (RRM) based on signal processing, we analysed ataxin proteins and identified the characteristic features corresponding to their biological activities. The RRM is a physico-mathematical model developed for analysis of protein interactions. By incorporating Smoothed Pseudo Wigner-Ville distribution (SPWV) in the RRM, we can define the active regions along the protein molecule. The results showed that our computational predictions correspond closely with the experimentally identified locations of the active/binding sites for ataxin-1 and ataxin-3 protein groups. The results obtained provide a valuable insight into the functional performance of ataxin proteins.

Investigating the interaction between oncogene and tumor suppressor protein

It is known that cancer develops when cells in a part of the body begin to grow out of control. Because cancer cells continue to grow and divide with no order, they never differentiate into the specific tissue, and thus, they are functionally different from normal cells. However, there are some genes that help to prevent cells' malignant behavior, and therefore, are referred to as tumor suppressor genes. Here, we have investigated the structural and functional relationships of p53, oncogene and interleukin 2 (IL2) proteins using the resonant recognition model (RRM), a physico-mathematical approach based on digital signal processing methods. In addition, using the RRM concepts, we have designed the peptide analoges that would exhibit tumor-suppression-like activity and be used in anticancer vaccine development.

Bioactive peptide design using the Resonant Recognition Model

With a large number of DNA and protein sequences already known, the crucial question is to find out how the biological function of these macromolecules is "written" in the sequence of nucleotides or amino acids. Biological processes in any living organism are based on selective interactions between particular bio-molecules, mostly proteins. The rules governing the coding of a protein's biological function, i.e. its ability to selectively interact with other molecules, are still not elucidated. In addition, with the rapid accumulation of databases of protein primary structures, there is an urgent need for theoretical approaches that are capable of analysing protein structure-function relationships. The Resonant Recognition Model (RRM) 12 is one attempt to identify the selectivity of protein interactions within the amino acid sequence. The RRM 12 is a physico-mathematical approach that interprets protein sequence linear information using digital signal processing methods. In the RRM the protein primary structure is represented as a numerical series by assigning to each amino acid in the sequence a physical parameter value relevant to the protein's biological activity. The RRM concept is based on the finding that there is a significant correlation between spectra of the numerical presentation of amino acids and their biological activity. Once the characteristic frequency for a particular protein function/interaction is identified, it is possible then to utilize the RRM approach to predict the amino acids in the protein sequence, which predominantly contribute to this frequency and thus, to the observed function, as well as to design de novo peptides having the desired periodicities. As was shown in our previous studies of fibroblast growth factor (FGF) peptidic antagonists 23 and human immunodeficiency virus (HIV) envelope agonists 24, such de novo designed peptides express desired biological function. This study utilises the RRM computational approach to the analysis of oncogene and proto-oncogene proteins. The results obtained have shown that the RRM is capable of identifying the differences between the oncogenic and proto-oncogenic proteins with the possibility of identifying the "cancer-causing" features within their protein primary structure. In addition, the rational design of bioactive peptide analogues displaying oncogenic or proto-oncogenic-like activity is presented here.

Phospholamban, a predicted candidate for early cardiac problem detection using signal processing techniques

Heart failure has been identified as a serious international problem, in particular for aging groups, posing both an increasing number of patients on waiting lists in countries susceptible with Medicare systems and increasing financial burdens. It may be imperative to develop a marker that can identify such problems at an early stage. It is believed that certain proteins have crucial roles in early detection of cardiovascular disease, the number one killer in United Arab Emirates. This might be accomplished by recognition of unusual features in protein candidates. Phospholamban (PLB) is a 52 amino acid phosphoprotein which regulates the calcium pump of cardiac sarcoplasmic reticulum (SR). During muscle contraction, PLB inhibits the Ca⁺⁺ pump. During muscle relaxation, it can be phosphorylated, removing the inhibition and allowing Ca⁺⁺ to be pumped back into SR. With the calcium pump disrupted, the heart muscle is probably weakened, resulting in congestive heart failure. Interleukin 6 (IL-6) is considered as a better predictor of heart attack in elderly people. It could serve as an early warning sign since its level increases early in the inflammatory process. Also, it has been established that myocyte enhancer factor 2A (MEF2A) plays a vital role in the development of cardiovascular problems like atherosclerosis and restenosis after angioplasty inflammation. In this paper, the resonance recognition method (RRM) has been employed to determine the characteristic frequencies of the above-mentioned proteins. It has been found that phospholamban and IL-6 share the same characteristic frequency, 0.3320 plusmn 0.0002 suggesting their common probable contribution to heart failure. Myocyte enhancer factor 2A does not share the same characteristic frequency. Hence, phospholamban is suggested as a highly probable early marker for cardiac problem detection.

Soluble CD40L versus myocyte enhancer factor: predicting a prominent marker for cardiovascular disease

Atherosclerosis (ACS) has set off the innovation of molecular markers measured in plasma or serum, and used for the identification of individuals at high risk of Coronary Heart Disease (CHD). In an attempt to improve cardiovascular risk prediction, considerable interest is focused on inflammatory biomarkers including Interleukin (IL)-6, Phospholamban (PLB), Myocyte enhancer factor 2A (MEF2A), and Soluble CD40 ligand. In this paper, signal-processing techniques predicted the characteristic frequencies of the above-mentioned proteins, and common binding sites. The CD40L characteristic frequency, 0.3555+/-0.0001, is correlated with Protease inhibitors and the second peak, 0.4531+/-0.0009, is closely related to Fgfs. This study also revealed that for MEF2A, the characteristic frequency, 0.0488+/-0.0001, is specific for enhancers DNA regulating sequences. The remaining frequencies, 0.3672 +/-0.0001 and 0.4648+/-0.0002, are characteristic of the Myocyte Protease inhibiting activity and SOS operator function. Furthermore, clinical data suggested that the increased levels of CD40L reliably identify the subgroup of patients with ACS who are at highest risk for cardiac events. It is suggested that CD40L is a most prominent candidate for early detection of cardiac disease.

Analyzing functional similarity of protein sequences with discrete wavelet transform

This paper applies discrete wavelet transform (DWT) with various protein substitution models to find functional similarity of proteins with low identity. A new metric, 'S' function, based on the DWT is proposed to measure the pair-wise similarity. We also develop a segmentation technique, combined with DWT, to handle long protein sequences. The results are compared with those using the pair-wise alignment and PSI-BLAST.

RRM analysis of protoporphyrinogen oxidase

Enzymes are crucial in accelerating metabolic reactions in living organisms. Protoporphyrinogen oxidase (PpOI) is an enzyme that catalyses the production of protoporphyrin IX (PpIX), a protein used in a cancer treatment known as photodynamic therapy (PDT). In this study, a structure-function analysis of PpOI was carried out using the Resonant Recognition Model (RRM), a physico-mathematical approach for analysis of proteins interactions. This method is based on the finding that the distribution of delocalised electron energies along the protein plays a crucial role in determining the protein's biological activity. Two digital signal processing (DSP) methods were used: Fourier Transform (FT) and Continuous Wavelet Transform (CWT). Here we have determined the characteristic frequencies and the "hot spot" amino acids, and predicted the location of proteins' active site(s). Several proteins that potentially belong to the PpOI functional group were also analysed to distinguish their viability in this role.

Protein sequence comparison based on the wavelet transform approach

A protein's chemical properties, the chain conformation, the function of the protein and its species specificity are determined by the information contained in the amino acid sequence. Proteins of similar functions have at some level sequential identical amino acid sequences. The closer the phylogenetic relationship, the more similar are the sequences. To find the similarities between two or more protein sequences is of great importance for protein sequence analysis. The differences in the amino acid sequences permit the construction of a family tree of evolution. In this work, a comparison method was devised that is capable of analysing a protein sequence 'hierarchically', i.e. it can examine a protein sequence at different spatial resolutions. Based on a wavelet decomposition of protein sequences and a cross-correlation study, a sequence-scale similarity concept is proposed for generating a similarity vector, which renders the comparison of two sequences feasible at different spatial resolutions (scales). This new similarity concept is an expansion of the conventional sequence similarity, which only takes into account the local pairwise amino acid match and ignores the information contained in coarser spatial resolutions.

Wavelet transforms for the characterization and detection of repeating motifs

The role of repeating motifs in protein structures is thought to be as modular building blocks which allow an economic way of constructing complex proteins. In this work novel wavelet transform analysis techniques are used to detect and characterize repeating motifs in protein sequence and structure data, where the Kyte-Doolittle hydrophobicity scale (Eta Phi) and relative accessible surface area (rASA) data provide residue information about the protein sequence and structure, respectively. We analyze a variety of repeating protein motifs, TIM barrels, propellor blades, coiled coils and leucine-rich repeat structures. Detection and characterization of these motifs is performed using techniques based on the continuous wavelet transform (CWT). Results indicate that the wavelet transform techniques developed herein are a promising approach for the detection and characterization of repeating motifs for both structural and in some instances sequence data.