afpCOOL: A tool for antifreeze protein prediction

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

A review of artificial intelligence-assisted omics techniques in plant defense: current trends and future directions

Plants intricately deploy defense systems to counter diverse biotic and abiotic stresses. Omics technologies, spanning genomics, transcriptomics, proteomics, and metabolomics, have revolutionized the exploration of plant defense mechanisms, unraveling molecular intricacies in response to various stressors. However, the complexity and scale of omics data necessitate sophisticated analytical tools for meaningful insights. This review delves into the application of artificial intelligence algorithms, particularly machine learning and deep learning, as promising approaches for deciphering complex omics data in plant defense research. The overview encompasses key omics techniques and addresses the challenges and limitations inherent in current AI-assisted omics approaches. Moreover, it contemplates potential future directions in this dynamic field. In summary, AI-assisted omics techniques present a robust toolkit, enabling a profound understanding of the molecular foundations of plant defense and paving the way for more effective crop protection strategies amidst climate change and emerging diseases.

Analysis of Ice-Binding Protein Evolution

Discovering novel ice-binding proteins (IBPs) is important for understanding the evolution of IBPs but it is difficult to determine where resources should be directed in the search for novel IBPs. For this reason, we developed a simple bioinformatic approach for aiding in the determination of where to direct efforts in the search for IBPs. First, BLAST is used to obtain a candidate list of putative IBPs. Next, phylogenetic trees are constructed to map the candidate list of putative IBPs to determine if any patterns are forming. These candidate putative IBPs and their patterns are then assessed through the production of ancestral sequences and reverse BLAST searches, in addition to the use of IBP calculators, to determine which sequences should be cut to produce the final putative IBP list. Finally, we explain an avenue to investigate these putative IBPs further for the development of hypotheses on their evolution.

Effect of antifreeze proteins on the freeze-thaw cycle of foods: fundamentals, mechanisms of action, current challenges and recommendations for future work

Freezing is widely used in food preservation, but if not carried out properly, ice crystals can multiply (nucleation) or grow (recrystallization) rapidly. This also affects thawing, causing structural damage and affecting overall quality. The objective of this review is to comprehensively study the cryoprotective effect of antifreeze proteins (AFPs), highlighting their role in the freeze-thaw process of food. The properties of AFPs are based on their thermal hysteresis capacity (THC), on the modification of crystal morphology and on the inhibition of ice recrystallization. The mechanism of action of AFPs is based on the adsorption-inhibition theory, but the specific role of hydrogen and hydrophobic bonds/residues and structural characteristics is also detailed. Because of the properties of AFPs, they have been successfully used to preserve the quality of a wide variety of refrigerated and frozen foods. Among the limitations of the use of AFPs, the high cost of production stands out, but currently there are solutions such as the use the production of recombinant proteins, cloning and chemical synthesis. Although in vitro, in vivo and human studies have shown that AFPs are non-toxic, their safety remains a matter of debate. Further studies are recommended to expand knowledge about AFPs, to reduce costs in their large-scale production, to understand their interaction with other food compounds and their possible effects on the consumer.

Molecular evidence of intertidal habitats selecting for repeated ice-binding protein evolution in invertebrates

Ice-binding proteins (IBPs) have evolved independently in multiple taxonomic groups to improve their survival at sub-zero temperatures. Intertidal invertebrates in temperate and polar regions frequently encounter sub-zero temperatures, yet there is little information on IBPs in these organisms. We hypothesized that there are far more IBPs than are currently known and that the occurrence of freezing in the intertidal zone selects for these proteins. We compiled a list of genome-sequenced invertebrates across multiple habitats and a list of known IBP sequences and used BLAST to identify a wide array of putative IBPs in those invertebrates. We found that the probability of an invertebrate species having an IBP was significantly greater in intertidal species than in those primarily found in open ocean or freshwater habitats. These intertidal IBPs had high sequence similarity to fish and tick antifreeze glycoproteins and fish type II antifreeze proteins. Previously established classifiers based on machine learning techniques further predicted ice-binding activity in the majority of our newly identified putative IBPs. We investigated the potential evolutionary origin of one putative IBP from the hard-shelled mussel Mytilus coruscus and suggest that it arose through gene duplication and neofunctionalization. We show that IBPs likely readily evolve in response to freezing risk and that there is an array of uncharacterized IBPs, and highlight the need for broader laboratory-based surveys of the diversity of ice-binding activity across diverse taxonomic and ecological groups.

AFP-CMBPred: Computational identification of antifreeze proteins by extending consensus sequences into multi-blocks evolutionary information

In extremely cold environments, living organisms like plants, animals, fishes, and microbes can die due to the intracellular ice formation in their bodies. To sustain life in such cold environments, some cold-blooded species produced Antifreeze proteins (AFPs), also called ice-binding proteins. AFPs are not only limited to the medical field but also have diverse significance in the area of biotechnology, agriculture, and the food industry. Different AFPs exhibit high heterogeneity in their structures and sequences. Keeping the significance of AFPs, several machine-learning-based models have been developed by scientists for the prediction of AFPs. However, due to the complex and diverse nature of AFPs, the prediction performance of the existing methods is limited. Therefore, it is highly indispensable for researchers to develop a reliable computational model that can accurately predict AFPs. In this connection, this study presents a novel predictor for AFPs, named AFP-CMBPred. The sequences of AFPs are formulated via four different feature representation methods, such as Amphiphilic pseudo amino acid composition (Amp-PseAAC), Dipeptide Deviation from Expected Mean (DDE), Multi-Blocks Position Specific Scoring Matrix (MB-PSSM), and Consensus Sequence-based on Multi-Blocks Position Specific Scoring Matrix (CS-MB-PSSM) to collect local and global descriptors. In the next step, the extracted feature vectors are evaluated via Support Vector Machine (SVM) and Random Forest (RF) based classification learners. The prediction performance of both classifiers is further assessed using three validation methods i.e., jackknife test, 10-fold cross-validation test, and independent test. After examining the prediction rates of all validation tests, it was found that our proposed model achieved the higher prediction accuracies of ∼2.65%, ∼2.84%, and ∼3.37% using jackknife, K-fold, and independent test, respectively. The experimental outcomes validate that our proposed "AFP-CMBPred" predictor secured the highest prediction results than the existing models for the identification of AFPs. It is further anticipated that our proposed AFP-CMBPred model will be considered a valuable tool in the research academia and drug development.

Prediction and analysis of antifreeze proteins

Antifreeze proteins (AFPs) are proteins that protect cellular fluids and body fluids from freezing by inhibiting the nucleation and growth of ice crystals and preventing ice recrystallization, thereby contributing to the maintenance of life in living organisms. They exist in fish, insects, microorganisms, and fungi. However, the number of known AFPs is currently limited, and it is essential to construct a reliable dataset of AFPs and develop a bioinformatics tool to predict AFPs. In this work, we first collected AFPs sequences from UniProtKB considering the reliability of annotations and, based on these datasets, developed a prediction system using random forest. We achieved accuracies of 0.961 and 0.947 for non-redundant sequences with less than 90% and 30% identities and achieved the accuracy of 0.953 for representative sequences for each species. Using the ability of random forest, we identified the sequence features that contributed to the prediction. Some sequence features were common to AFPs from different species. These features include the Cys content, Ala-Ala content, Trp-Gly content, and the amino acids' distribution related to the disorder propensity. The computer program and the dataset developed in this work are available from the GitHub site: https://github.com/ryomiya/Prediction-and-analysis-of-antifreeze-proteins.

PoGB-pred: Prediction of Antifreeze Proteins Sequences Using Amino Acid Composition with Feature Selection Followed by a Sequential-based Ensemble Approach

Background:

Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very cold areas. With the help of these proteins, the cold water organisms can survive below zero temperature and resist the water crystallization process, which may cause the rupture in the internal cells and tissues. AFP’s have also attracted attention and interest in food industries and cryopreservation.

Objective:

With the increase in the availability of genomic sequence data of protein, an automated and sophisticated tool for AFP recognition and identification is in dire need. The sequence and structures of AFP are highly distinct, therefore, most of the proposed methods fail to show promising results on different structures. A consolidated method is proposed to produce the competitive performance on highly distinct AFP structure.

Methods:

In this study, machine learning-based algorithms including Principal Component Analysis (PCA) followed by Gradient Boosting (GB) were proposed to be used for anti-freeze protein identification. To analyze the performance and validation of the proposed model, various combinations of two segments' composition of amino acid and dipeptides are used. PCA, in particular, is proposed for dimension reduction and high variance retaining of data, which is followed by an ensemble method named gradient boosting for modeling and classification.

Results:

The proposed method obtained the superfluous performance on PDB, Pfam and Uniprot dataset as compared with the RAFP-Pred method. In experiment-3, by utilizing only 150 PCA components a high accuracy of 89.63 was achieved which is superior to the 87.41 utilizing 300 significant features reported for the RAFP-Pred method. Experiment-2 is conducted using two different dataset such that non-AFP from the PISCES server and AFPs from Protein data bank. In this experiment-2, our proposed method attained high sensitivity of 79.16 which is 12.50 better than state-of-the-art the RAFP-pred method.

Conclusion:

AFPs have a common function with distinct structure. Therefore, the development of a single model for different sequences often fails to AFPs. A robust results have been shown by our proposed model on the diversity of training and testing dataset. The results of the proposed model outperformed compared to the previous AFPs prediction method such as RAFP-Pred. Our model consists of PCA for dimension reduction followed by gradient boosting for classification. Due to simplicity, scalability properties and high performance result our model can be easily extended for analyzing the proteomic and genomic dataset.

Identifying Antifreeze Proteins Based on Key Evolutionary Information

Antifreeze proteins are important antifreeze materials that have been widely used in industry, including in cryopreservation, de-icing, and food storage applications. However, the quantity of some commercially produced antifreeze proteins is insufficient for large-scale industrial applications. Further, many antifreeze proteins have properties such as cytotoxicity, severely hindering their applications. Understanding the mechanisms underlying the protein-ice interactions and identifying novel antifreeze proteins are, therefore, urgently needed. In this study, to uncover the mechanisms underlying protein-ice interactions and provide an efficient and accurate tool for identifying antifreeze proteins, we assessed various evolutionary features based on position-specific scoring matrices (PSSMs) and evaluated their importance for discriminating of antifreeze and non-antifreeze proteins. We then parsimoniously selected seven key features with the highest importance. We found that the selected features showed opposite tendencies (regarding the conservation of certain amino acids) between antifreeze and non-antifreeze proteins. Five out of the seven features had relatively high contributions to the discrimination of antifreeze and non-antifreeze proteins, as revealed by a principal component analysis, i.e., the conservation of the replacement of Cys, Trp, and Gly in antifreeze proteins by Ala, Met, and Ala, respectively, in the related proteins, and the conservation of the replacement of Arg in non-antifreeze proteins by Ser and Arg in the related proteins. Based on the seven parsimoniously selected key features, we established a classifier using support vector machine, which outperformed the state-of-the-art tools. These results suggest that understanding evolutionary information is crucial to designing accurate automated methods for discriminating antifreeze and non-antifreeze proteins. Our classifier, therefore, is an efficient tool for annotating new proteins with antifreeze functions based on sequence information and can facilitate their application in industry.

The properties, biotechnologies, and applications of antifreeze proteins

By natural selection, organisms evolve different solutions to cope with extremely cold weather. The emergence of an antifreeze protein gene is one of the most momentous solutions. Antifreeze proteins possess an importantly functional ability for organisms to survive in cold environments and are widely found in various cold-tolerant species. In this review, we summarize the origin of antifreeze proteins, describe the diversity of their species-specific properties and functions, and highlight the related biotechnology on the basis of both laboratory tests and bioinformatics analysis. The most recent advances in the applications of antifreeze proteins are also discussed. We expect that this systematic review will contribute to the comprehensive knowledge of antifreeze proteins to readers.

Water in protein hydration and ligand recognition

This review describes selected basics of water in biomolecular recognition. We focus on a qualitative understanding of the most important physical aspects, how these change in magnitude between bulk water and protein environment, and how the roles that water plays for proteins arise from them. These roles include mechanical support, thermal coupling, dielectric screening, mass and charge transport, and the competition with a ligand for the occupation of a binding site. The presence or absence of water has ramifications that range from the thermodynamic binding signature of a single ligand up to cellular survival. The large inhomogeneity in water density, polarity and mobility around a solute is hard to assess in experiment. This is a source of many difficulties in the solvation of protein models and computational studies that attempt to elucidate or predict ligand recognition. The influence of water in a protein binding site on the experimental enthalpic and entropic signature of ligand binding is still a point of much debate. The strong water‐water interaction in enthalpic terms is counteracted by a water molecule's high mobility in entropic terms. The complete arrest of a water molecule's mobility sets a limit on the entropic contribution of a water displacement process, while the solvent environment sets limits on ligand reactivity.