Amphiphilic Pseudo Amino Acid Composition Research Articles

LLM4THP: a computing tool to identify tumor homing peptides by molecular and sequence representation of large language model based on two-layer ensemble model strategy

Tumor homing peptides (THPs) have a distinctive capacity to specifically attach to tumor cells, providing a promising approach for targeted cancer treatment and detection. Although THPs have the potential for significant impact, their detection by conventional methods is both time-consuming and expensive. To tackle this issue, we provide LLM4THP, an innovative computational approach that utilizes large language models (LLMs) to quickly and effectively detect THPs. LLM4THP utilizes two protein LLMs, ESM2 and Prot_T5_XL_UniRef50, to encode peptide sequences. This allows for the capture of complex patterns and relationships within the peptide data. In addition, we utilize inherent sequence characteristics such as Amino Acid Composition (AAC), Pseudo Amino Acid Composition (PAAC), Amphiphilic Pseudo Amino Acid Composition (APAAC), and Composition, Transition, and Distribution (CTD) to improve the representation of peptides. The RDKitDescriptors feature representation approach transforms peptide sequences into molecular objects and computes chemical characteristics, resulting in enhanced THP identification. The LLM4THP ensemble strategy incorporates various features into a two-layer learning architecture. The first layer consists of LightGBM, XGBoost, Random Forest, and Extremely Randomized Trees, which generate a set of meta results. The second layer utilizes Logistic Regression to further refine the identification of sequences as either THP or non-THP. LLM4THP exhibits exceptional performance compared to the most advanced methods, showcasing enhancements in accuracy, Matthew’s correlation coefficient, F1 score, area under the curve, and average precision. The source code and dataset can be accessed at the following URL: https://github.com/abcair/LLM4THP.

UmamiPreDL: Deep learning model for umami taste prediction of peptides using BERT and CNN

Taste is crucial in driving food choice and preference. Umami is one of the basic tastes defined by characteristic deliciousness and mouthfulness that it imparts to foods. Identification of ingredients to enhance umami taste is of significant value to food industry. Various models have been shown to predict umami taste using feature encodings derived from traditional molecular descriptors such as amphiphilic pseudo-amino acid composition, dipeptide composition, and composition-transition-distribution. Highest reported accuracy of 90.5 % was recently achieved through novel model architecture. Here, we propose use of biological sequence transformers such as ProtBert and ESM2, trained on the Uniref databases, as the feature encoders block. With combination of 2 encoders and 2 classifiers, 4 model architectures were developed. Among the 4 models, ProtBert-CNN model outperformed other models with accuracy of 95 % on 5-fold cross validation data and 94 % on independent data.

Stack-AAgP: Computational prediction and interpretation of anti-angiogenic peptides using a meta-learning framework

Background:Angiogenesis plays a vital role in the pathogenesis of several human diseases, particularly in the case of solid tumors. In the realm of cancer treatment, recent investigations into peptides with anti-angiogenic properties have yielded encouraging outcomes, thereby creating a hopeful therapeutic avenue for the treatment of cancer. Therefore, correctly identifying the anti-angiogenic peptides is extremely important in comprehending their biophysical and biochemical traits, laying the groundwork for uncovering novel drugs to combat cancer. Methods:In this work, we present a novel ensemble-learning-based model, Stack-AAgP, specifically designed for the accurate identification and interpretation of anti-angiogenic peptides (AAPs). Initially, a feature representation approach is employed, generating 24 baseline models through six machine learning algorithms (random forest [RF], extra tree classifier [ETC], extreme gradient boosting [XGB], light gradient boosting machine [LGBM], CatBoost, and SVM) and four feature encodings (pseudo-amino acid composition [PAAC], amphiphilic pseudo-amino acid composition [APAAC], composition of k-spaced amino acid pairs [CKSAAP], and quasi-sequence-order [QSOrder]). Subsequently, the output (predicted probabilities) from 24 baseline models was inputted into the same six machine-learning classifiers to generate their respective meta-classifiers. Finally, the meta-classifiers were stacked together using the ensemble-learning framework to construct the final predictive model. Results:Findings from the independent test demonstrate that Stack-AAgP outperforms the state-of-the-art methods by a considerable margin. Systematic experiments were conducted to assess the influence of hyperparameters on the proposed model. Our model, Stack-AAgP, was evaluated on the independent NT15 dataset, revealing superiority over existing predictors with an accuracy improvement ranging from 5% to 7.5% and an increase in Matthews Correlation Coefficient (MCC) from 7.2% to 12.2%.

Accelerating the identification of the allergenic potential of plant proteins using a stacked ensemble-learning framework

ABSTRACT: Plant-allergenic proteins (PAPs) have the potential to induce allergic reactions in certain individuals. While these proteins are generally innocuous for the majority of people, they can elicit an immune response in those with particular sensitivities. Thus, screening and prioritizing the allergenic potential of plant proteins is indispensable for the development of diagnostic tools, therapeutic interventions or medications to treat allergic reactions. However, investigating the allergenic potential of plant proteins based on experimental methods is costly and labour-intensive. Therefore, we develop StackPAP, a three-layer stacking ensemble framework for accurate large-scale identification of PAPs. In StackPAP, at the first layer, we conducted a comprehensive analysis of an extensive set of feature descriptors. Subsequently, we selected and fused five potential sequence-based feature descriptors, including amphiphilic pseudo-amino acid composition, dipeptide deviation from expected mean, amino acid composition, pseudo amino acid composition and dipeptide composition. Additionally, we applied an efficient genetic algorithm (GA-SAR) to determine informative feature sets. In the second layer, 12 powerful machine learning (ML) methods, in combination with all the informative feature sets, were employed to construct a pool of base classifiers. Finally, 13 potential base classifiers were selected using the GA-SAR method and combined to develop the final meta-classifier. Our experimental results revealed the promising prediction performance of StackPAP, with an accuracy, Matthew’s correlation coefficient and AUC of 0.984, 0.969 and 0.993, respectively, as judged by the independent test dataset. In conclusion, both cross-validation and independent test results indicated the superior performance of StackPAP compared with several ML-based classifiers. To accelerate the identification of the allergenicity of plant proteins, we developed a user-friendly web server for StackPAP (https://pmlabqsar.pythonanywhere.com/StackPAP). We anticipate that StackPAP will be an efficient and useful tool for rapidly screening PAPs from a vast number of plant proteins. Communicated by Ramaswamy H. Sarma

Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.

Introduction: Apoptosis proteins play an important role in the process of cell apoptosis, which makes the rate of cell proliferation and death reach a relative balance. The function of apoptosis protein is closely related to its subcellular location, it is of great significance to study the subcellular locations of apoptosis proteins. Many efforts in bioinformatics research have been aimed at predicting their subcellular location. However, the subcellular localization of apoptotic proteins needs to be carefully studied. Methods: In this paper, based on amphiphilic pseudo amino acid composition and support vector machine algorithm, a new method was proposed for the prediction of apoptosis proteins\x{2019} subcellular location. Results and Discussion: The method achieved good performance on three data sets. The Jackknife test accuracy of the three data sets reached 90.5%, 93.9% and 84.0%, respectively. Compared with previous methods, the prediction accuracies of APACC_SVM were improved.

IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.

Motivation. Immunoglobulin proteins (IGP) (also called antibodies) are glycoproteins that act as B-cell receptors against external or internal antigens like viruses and bacteria. IGPs play a significant role in diverse cellular processes ranging from adhesion to cell recognition. IGP identifications via the in-silico approach are faster and more cost-effective than wet-lab technological methods. Methods. In this study, we developed an intelligent theoretical deep learning framework, "IGPred-HDnet" for the discrimination of IGPs and non-IGPs. Three types of promising descriptors are feature extraction based on graphical and statistical features (FEGS), amphiphilic pseudo-amino acid composition (Amp-PseAAC), and dipeptide composition (DPC) to extract the graphical, physicochemical, and sequential features. Next, the extracted attributes are evaluated through machine learning, i.e., decision tree (DT), support vector machine (SVM), k-nearest neighbour (KNN), and hierarchical deep network (HDnet) classifiers. The proposed predictor IGPred-HDnet was trained and tested using a 10-fold cross-validation and independent test. Results and Conclusion. The success rates in terms of accuracy (ACC) and Matthew's correlation coefficient (MCC) of IGPred-HDnet on training and independent dataset (Dtrain Dtest) are ACC = 98.00%, 99.10%, and MCC = 0.958, and 0.980 points, respectively. The empirical outcomes demonstrate that the IGPred-HDnet model efficacy on both datasets using the novel FEGS feature and HDnet algorithm achieved superior predictions to other existing computational models. We hope this research will provide great insights into the large-scale identification of IGPs and pharmaceutical companies in new drug design.

MP-VHPPI: Meta predictor for viral host protein-protein interaction prediction in multiple hosts and viruses.

Viral-host protein-protein interaction (VHPPI) prediction is essential to decoding molecular mechanisms of viral pathogens and host immunity processes that eventually help to control the propagation of viral diseases and to design optimized therapeutics. Multiple AI-based predictors have been developed to predict diverse VHPPIs across a wide range of viruses and hosts, however, these predictors produce better performance only for specific types of hosts and viruses. The prime objective of this research is to develop a robust meta predictor (MP-VHPPI) capable of more accurately predicting VHPPI across multiple hosts and viruses. The proposed meta predictor makes use of two well-known encoding methods Amphiphilic Pseudo-Amino Acid Composition (APAAC) and Quasi-sequence (QS) Order that capture amino acids sequence order and distributional information to most effectively generate the numerical representation of complete viral-host raw protein sequences. Feature agglomeration method is utilized to transform the original feature space into a more informative feature space. Random forest (RF) and Extra tree (ET) classifiers are trained on optimized feature space of both APAAC and QS order separate encoders and by combining both encodings. Further predictions of both classifiers are utilized to feed the Support Vector Machine (SVM) classifier that makes final predictions. The proposed meta predictor is evaluated over 7 different benchmark datasets, where it outperforms existing VHPPI predictors with an average performance of 3.07, 6.07, 2.95, and 2.85% in terms of accuracy, Mathews correlation coefficient, precision, and sensitivity, respectively. To facilitate the scientific community, the MP-VHPPI web server is available at https://sds_genetic_analysis.opendfki.de/MP-VHPPI/.

Machine learning-based approach for prediction of ion channels and their subclasses.

Ion channels are ion-permeable protein pores that are found in all cell lipid membranes. Distinct ion channels play multiple roles in biological processes. Proteomic data is fast accumulating as a result of the fast growth of mass spectrometry and giving us the chance to comprehensively explore ion channel classes along with their subclasses. This paper proposes an eXtreme Gradient Boosting(XGBoost)-based method to estimate the ion channel classes and their subclasses. Here, 12feature vectors are applied to better characterize protein sequences like amino acid composition, pseudo-amino acid composition, normalized moreau-broto autocorrelation, amphiphilic pseudo-amino acid composition, dipeptide composition, Geary autocorrelation, tripeptide composition, sequence-order-coupling number, composition/transition/distribution, conjoint triad, moran autocorrelation, quasi-sequence-order descriptors. Here, a total of 9920 features are extracted from the protein sequence. The principal component analysis is applied to determine the optimal number of features to optimize the performance. In 10-fold cross-validation the proposed XGBoost based approach with optimal 50 features achieved accuracy of 100%, 98.70%, 98.77%, 97.26%, 87.40%, 97.39%, 98.03%, 96.42%, and F1-Score of 100%, 99%, 99%, 97%, 87%, 97%, 98%, 97%, for prediction of ion channel and nonion channel, voltage-gated and ligand-gated ion channels, subclasses of voltage-gated ion channels (VGICs), subclasses of ligand-gated ion channels (LGICs), subclasses of voltage-gated calcium channels (VGCCs), subclasses of voltage-gated potassium channels (VGKCs), subclasses of voltage-gated sodium channels (VGSCs), and subclasses of voltage-gated chloride channels, respectively. Here the proposed approach also compares with the other approaches such as support vector machine, k-nearest neighbor, Gaussian Naïve Bayes,and random forest and also compares with existing methods such as support vector machine(SVM) with maximum relevance maximum distancewith an accuracy of 86.6%, 83.7%, and 85.1%, for ion channels, non-ion channels and overall respectively and SVM with radial basis functionkernel-based method with an accuracy of 100%, 97% and 99.9% for ion channels, nonion channels, and overall accuracy, respectively.

UMPred-FRL: A New Approach for Accurate Prediction of Umami Peptides Using Feature Representation Learning.

Umami ingredients have been identified as important factors in food seasoning and production. Traditional experimental methods for characterizing peptides exhibiting umami sensory properties (umami peptides) are time-consuming, laborious, and costly. As a result, it is preferable to develop computational tools for the large-scale identification of available sequences in order to identify novel peptides with umami sensory properties. Although a computational tool has been developed for this purpose, its predictive performance is still insufficient. In this study, we use a feature representation learning approach to create a novel machine-learning meta-predictor called UMPred-FRL for improved umami peptide identification. We combined six well-known machine learning algorithms (extremely randomized trees, k-nearest neighbor, logistic regression, partial least squares, random forest, and support vector machine) with seven different feature encodings (amino acid composition, amphiphilic pseudo-amino acid composition, dipeptide composition, composition-transition-distribution, and pseudo-amino acid composition) to develop the final meta-predictor. Extensive experimental results demonstrated that UMPred-FRL was effective and achieved more accurate performance on the benchmark dataset compared to its baseline models, and consistently outperformed the existing method on the independent test dataset. Finally, to aid in the high-throughput identification of umami peptides, the UMPred-FRL web server was established and made freely available online. It is expected that UMPred-FRL will be a powerful tool for the cost-effective large-scale screening of candidate peptides with potential umami sensory properties.

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.

Predicting Drug-Target Interactions with Electrotopological State Fingerprints and Amphiphilic Pseudo Amino Acid Composition.

The task of drug-target interaction (DTI) prediction plays important roles in drug development. The experimental methods in DTIs are time-consuming, expensive and challenging. To solve these problems, machine learning-based methods are introduced, which are restricted by effective feature extraction and negative sampling. In this work, features with electrotopological state (E-state) fingerprints for drugs and amphiphilic pseudo amino acid composition (APAAC) for target proteins are tested. E-state fingerprints are extracted based on both molecular electronic and topological features with the same metric. APAAC is an extension of amino acid composition (AAC), which is calculated based on hydrophilic and hydrophobic characters to construct sequence order information. Using the combination of these feature pairs, the prediction model is established by support vector machines. In order to enhance the effectiveness of features, a distance-based negative sampling is proposed to obtain reliable negative samples. It is shown that the prediction results of area under curve for Receiver Operating Characteristic (AUC) are above 98.5% for all the three datasets in this work. The comparison of state-of-the-art methods demonstrates the effectiveness and efficiency of proposed method, which will be helpful for further drug development.

Discrimination of Golgi Proteins Through Efficient Exploitation of Hybrid Feature Spaces Coupled With SMOTE and Ensemble of Support Vector Machine

Many organelles inside and outside a living cell depend on the perfect behavior of Golgi apparatus for smooth and normal functioning. Its poor performance may lead to many inheritable diseases like diabetes and cancer. Therefore, it is highly crucial to detect any strange behavior of Golgi apparatus in advance. Accurate discrimination of <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">cis</i> -Golgi from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">trans</i> -Golgi proteins surely helps researchers identify the role of Golgi proteins in various diseases and assist pharmacists in drug development. In this work, various hybrid models of Bi-Profile Bayes, Bigram PSSM, Di-Peptide Composition, and Split Amphiphilic Pseudo Amino Acid Composition with SMOTE oversampling technique have been employed to discriminate Golgi protein types. Multiple linear Support Vector Machines have been used to exploit the discrimination power of these models. The proposed prediction system: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Golgi-predictor</i> has shown significant performance and achieved promising results compared to other existing state-of-the-art techniques. Through the 10-fold cross-validation, the proposed system achieved an accuracy value of 97.6%, sensitivity value of 98.8%, specificity value of 96.5%, G-mean value of 97.6%, MCC value of 0.95, and F-score value of 0.97. Similarly, through the jackknife cross-validation, the achieved values for accuracy, sensitivity, specificity, G-mean, MCC, and F-score are respectively, 96.5%, 97.8%, 95.2%, 96.4%, 0.93, and 0.96. Moreover, through the independent dataset testing, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Golgi-predictor</i> demonstrated significant enhancement in performance over other techniques. The proposed methodology aims at supporting drug designers in pharmaceutical industry and assisting researchers from the fields of bioinformatics and computational biology towards better innovation in predicting the behavior of Golgi proteins.

IDTi-CSsmoteB: Identification of Drug–Target Interaction Based on Drug Chemical Structure and Protein Sequence Using XGBoost With Over-Sampling Technique SMOTE

Identifying interaction between drug and protein is a crucial challenge in drug discovery, which can lead the researchers to develop novel drug compounds or new target proteins for the existing drugs. The determination of drug-target interactions (DTIs) is an extremely time-consuming, costly, and tedious task with wet-lab experiments. To date, multiple computational techniques have been presented to simplify the drug discovery process, but a huge number of interactions are still undiscovered. Furthermore, a class imbalance is a critical challenge regarding this experiment which can significantly degrade the classification accuracy that has not been effectively addressed yet. In this paper, we proposed a novel high-throughput computational model, called iDTi-CSsmoteB, for identification of DTIs based on drug chemical structures and protein sequences. More specifically, the protein sequence is extracted through position-specific scoring matrix (PSSM)-Bigram, amphiphilic pseudo amino acid composition (AM-PseAAC) and dipeptide PseAAC descriptors which represents evolutionary and sequence information. The drug chemical structure is represented as a molecular substructure fingerprint (MSF) which describes the existence of the functional fragments or groups. Finally, we used the over-sampling SMOTE technique to overcome the imbalance issue of the datasets and applied XGBoost algorithm as a classifier to predict DTIs. To evaluate the performance of iDTi-CSsmoteB, several experiments have been conducted on four benchmark datasets, namely, enzyme, ion channel, GPCR, and nuclear receptor based on fivefold cross validation. The experimental analysis exhibits that our model outperforms similar methods in terms of area under the ROC (auROC) curve. In addition, our achieved results indicate the effectiveness of the feature extraction techniques, balancing methods, and classifier for predicting the DTIs which can provide substance for new drug development. iDTi-CSsmoteB webserver is available online at http://idticssmoteb-uestc.me/.

Ensemble learning for protein multiplex subcellular localization prediction based on weighted KNN with different features

As an important attribute of proteins, protein subcellular location(s) can provide valuable information about their functions. Determining protein subcellular locations using experimental methods are usually expensive and time-consuming. Over the years, a variety of computational approaches have been developed to predict protein subcellular locations based on knowledge of known protein locations. However, the problem is inherently hard, especially for proteins that can exist at multiple subcellular locations. Further studies are still in great need in this area. In this paper, we propose an ensemble learning framework that utilizes a modified Weighted K-Nearest Neighbors (WKNN) as the basic learning algorithm. Two different types of features are considered and extracted from training data, which are based on protein amino acid compositions (Amphiphilic Pseudo Amino Acid Composition, or AmPseAAC) and protein sequence similarities (Protein Similarity Measure, or PSM), respectively. Two individual classifiers are trained separately based on these two types of features and each assigns a probability distribution over different locations to a query protein. Based on the outputs of the two base classifiers, a novel ensemble strategy named Maximized Probability on Label (MPoL) is proposed. The strategy produces a final set of protein locations for each protein by integrating prediction results of the base classifiers through an optimization procedure. To measure the prediction quality of the proposed approach, two different types of evaluation metrics, example-based metrics and label-based metrics, are used. To evaluate the performance of our approach objectively, we compare its results with those predicted by another popular method named iLoc-Animal on a benchmark dataset through cross-validation. Results show that in terms of absolute true success rate on multi-location prediction, MPoL has achieved much better results than iLoc-Animal. It implies that the proposed method has some potential to solve a diverse set of multi-label learning problems.

IACP-GAEnsC: Evolutionary genetic algorithm based ensemble classification of anticancer peptides by utilizing hybrid feature space.

Cancer is a fatal disease, responsible for one-quarter of all deaths in developed countries. Traditional anticancer therapies such as, chemotherapy and radiation, are highly expensive, susceptible to errors and ineffective techniques. These conventional techniques induce severe side-effects on human cells. Due to perilous impact of cancer, the development of an accurate and highly efficient intelligent computational model is desirable for identification of anticancer peptides. In this paper, evolutionary intelligent genetic algorithm-based ensemble model, 'iACP-GAEnsC', is proposed for the identification of anticancer peptides. In this model, the protein sequences are formulated, using three different discrete feature representation methods, i.e., amphiphilic Pseudo amino acid composition, g-Gap dipeptide composition, and Reduce amino acid alphabet composition. The performance of the extracted feature spaces are investigated separately and then merged to exhibit the significance of hybridization. In addition, the predicted results of individual classifiers are combined together, using optimized genetic algorithm and simple majority technique in order to enhance the true classification rate. It is observed that genetic algorithm-based ensemble classification outperforms than individual classifiers as well as simple majority voting base ensemble. The performance of genetic algorithm-based ensemble classification is highly reported on hybrid feature space, with an accuracy of 96.45%. In comparison to the existing techniques, 'iACP-GAEnsC' model has achieved remarkable improvement in terms of various performance metrics. Based on the simulation results, it is observed that 'iACP-GAEnsC' model might be a leading tool in the field of drug design and proteomics for researchers.

Pred-binding: large-scale protein–ligand binding affinity prediction

Drug target interactions (DTIs) are crucial in pharmacology and drug discovery. Presently, experimental determination of compound–protein interactions remains challenging because of funding investment and difficulties of purifying proteins. In this study, we proposed two in silico models based on support vector machine (SVM) and random forest (RF), using 1589 molecular descriptors and 1080 protein descriptors in 9948 ligand–protein pairs to predict DTIs that were quantified by Ki values. The cross-validation coefficient of determination of 0.6079 for SVM and 0.6267 for RF were obtained, respectively. In addition, the two-dimensional (2D) autocorrelation, topological charge indices and three-dimensional (3D)-MoRSE descriptors of compounds, the autocorrelation descriptors and the amphiphilic pseudo-amino acid composition of protein are found most important for Ki predictions. These models provide a new opportunity for the prediction of ligand–receptor interactions that will facilitate the target discovery and toxicity evaluation in drug development.

PaPI: pseudo amino acid composition to score human protein-coding variants.

BackgroundHigh throughput sequencing technologies are able to identify the whole genomic variation of an individual. Gene-targeted and whole-exome experiments are mainly focused on coding sequence variants related to a single or multiple nucleotides. The analysis of the biological significance of this multitude of genomic variant is challenging and computational demanding.ResultsWe present PaPI, a new machine-learning approach to classify and score human coding variants by estimating the probability to damage their protein-related function. The novelty of this approach consists in using pseudo amino acid composition through which wild and mutated protein sequences are represented in a discrete model. A machine learning classifier has been trained on a set of known deleterious and benign coding variants with the aim to score unobserved variants by taking into account hidden sequence patterns in human genome potentially leading to diseases. We show how the combination of amphiphilic pseudo amino acid composition, evolutionary conservation and homologous proteins based methods outperforms several prediction algorithms and it is also able to score complex variants such as deletions, insertions and indels.ConclusionsThis paper describes a machine-learning approach to predict the deleteriousness of human coding variants. A freely available web application (http://papi.unipv.it) has been developed with the presented method, able to score up to thousands variants in a single run.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0554-8) contains supplementary material, which is available to authorized users.

Discriminating Outer Membrane Proteins with Fuzzy K-Nearest Neighbor Algorithms Based on the General Form of Chou’s PseAAC

Outer membrane proteins (OMPs) play important roles in cell biology. In addition, OMPs are targeted by multiple drugs. The identification of OMPs from genomic sequences and successful prediction of their secondary and tertiary structures is a challenging task due to short membrane-spanning regions with high variation in properties. Therefore, an effective and accurate silico method for discrimination of OMPs from their primary sequences is needed. In this paper, we have analyzed the performance of various machine learning mechanisms for discriminating OMPs such as: Genetic Programming, K-nearest Neighbor, and Fuzzy K-nearest Neighbor (Fuzzy K-NN) in conjunction with discrete methods such as: Amino acid composition, Amphiphilic Pseudo amino acid composition, Split amino acid composition (SAAC), and hybrid versions of these methods. The performance of the classifiers is evaluated by two datasets using 5-fold crossvalidation. After the simulation, we have observed that Fuzzy K-NN using SAAC based-features makes it quite effective in discriminating OMPs. Fuzzy K-NN achieves the highest success rates of 99.00% accuracy for discriminating OMPs from non-OMPs and 98.77% and 98.28% accuracies from α-helix membrane and globular proteins, respectively on dataset1. While on dataset2, Fuzzy K-NN achieves 99.55%, 99.90%, and 99.81% accuracies for discriminating OMPs from non- OMPs, α-helix membrane, and globular proteins, respectively. It is observed that the classification performance of our proposed method is satisfactory and is better than the existing methods. Thus, it might be an effective tool for high throughput innovation of OMPs.

Prediction of Protein Subcellular Multi-Localization Based on the General form of Chou’s Pseudo Amino Acid Composition

Many proteins bear multi-locational characteristics, and this phenomenon is closely related to biological function. However, most of the existing methods can only deal with single-location proteins. Therefore, an automatic and reliable ensemble classifier for protein subcellular multi-localization is needed. We propose a new ensemble classifier combining the KNN (K-nearest neighbour) and SVM (support vector machine) algorithms to predict the subcellular localization of eukaryotic, Gram-negative bacterial and viral proteins based on the general form of Chou's pseudo amino acid composition, i.e., GO (gene ontology) annotations, dipeptide composition and AmPseAAC (Amphiphilic pseudo amino acid composition). This ensemble classifier was developed by fusing many basic individual classifiers through a voting system. The overall prediction accuracies obtained by the KNN-SVM ensemble classifier are 95.22, 93.47 and 80.72% for the eukaryotic, Gram-negative bacterial and viral proteins, respectively. Our prediction accuracies are significantly higher than those by previous methods and reveal that our strategy better predicts subcellular locations of multi-location proteins.

CE-PLoc: An ensemble classifier for predicting protein subcellular locations by fusing different modes of pseudo amino acid composition

Precise information about protein locations in a cell facilitates in the understanding of the function of a protein and its interaction in the cellular environment. This information further helps in the study of the specific metabolic pathways and other biological processes. We propose an ensemble approach called “ CE-PLoc” for predicting subcellular locations based on fusion of individual classifiers. The proposed approach utilizes features obtained from both dipeptide composition ( DC) and amphiphilic pseudo amino acid composition ( PseAAC) based feature extraction strategies. Different feature spaces are obtained by varying the dimensionality using PseAAC for a selected base learner. The performance of the individual learning mechanisms such as support vector machine, nearest neighbor, probabilistic neural network, covariant discriminant, which are trained using PseAAC based features is first analyzed. Classifiers are developed using same learning mechanism but trained on PseAAC based feature spaces of varying dimensions. These classifiers are combined through voting strategy and an improvement in prediction performance is achieved. Prediction performance is further enhanced by developing CE-PLoc through the combination of different learning mechanisms trained on both DC based feature space and PseAAC based feature spaces of varying dimensions. The predictive performance of proposed CE-PLoc is evaluated for two benchmark datasets of protein subcellular locations using accuracy, MCC, and Q-statistics. Using the jackknife test, prediction accuracies of 81.47 and 83.99% are obtained for 12 and 14 subcellular locations datasets, respectively. In case of independent dataset test, prediction accuracies are 87.04 and 87.33% for 12 and 14 class datasets, respectively.