Multi-class protein fold classification using a new ensemble machine learning approach.

Protein structure classification represents an important process in understanding the associations between sequence and structure as well as possible functional and evolutionary relationships. Recent structural genomics initiatives and other high-throughput experiments have populated the biological databases at a rapid pace. The amount of structural data has made traditional methods such as manual inspection of the protein structure become impossible. Machine learning has been widely applied to bioinformatics and has gained a lot of success in this research area. This work proposes a novel ensemble machine learning method that improves the coverage of the classifiers under the multi-class imbalanced sample sets by integrating knowledge induced from different base classifiers, and we illustrate this idea in classifying multi-class SCOP protein fold data. We have compared our approach with PART and show that our method improves the sensitivity of the classifier in protein fold classification. Furthermore, we have extended this method to learning over multiple data types, preserving the independence of their corresponding data sources, and show that our new approach performs at least as well as the traditional technique over a single joined data source. These experimental results are encouraging, and can be applied to other bioinformatics problems similarly characterised by multi-class imbalanced data sets held in multiple data sources

Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Prediction of aptamer-protein interacting pairs using an ensemble classifier in combination with various protein sequence attributes

The ranked feature list given by the Relief algorithm. Within the list, a feature with a smaller index indicates that it is more important for aptamer-protein interacting pair prediction. Such a list of ranked features are used to establish the optimal feature set in the IFS procedure. (XLS 56.5 kb

A NEW METHODOLOGY FOR IDENTIFYING INTERFACE RESIDUES INVOLVED IN BINDING PROTEIN COMPLEXES

Genome-sequencing projects with advanced technologies have rapidly increased the amount of protein sequences, and demands for identifying protein interaction sites are significantly increased due to its impact on understanding cellular process, biochemical events and drug design studies. However, the capacity of current wet laboratory techniques is not enough to handle the exponentially growing protein sequence data; therefore, sequence based predictive methods identifying protein interaction sites have drawn increasing interest. In this article, a new predictive model which can be valuable as a first approach for guiding experimental methods investigating protein-protein interactions and localizing the specific interface residues is proposed. The proposed method extracts a wide range of features from protein sequences. Random forests framework is newly redesigned to effectively utilize these features and the problems of imbalanced data classification commonly encountered in binding site predictions. The method is evaluated with 2,829 interface residues and 24,616 non-interface residues extracted from 99 polypeptide chains in the Protein Data Bank. The experimental results show that the proposed method performs significantly better than two other conventional predictive methods and can reliably predict residues involved in protein interaction sites. As blind tests, the proposed method predicts interaction sites and constructs three protein complexes: the DnaK molecular chaperone system, 1YUW and 1DKG, which provide new insight into the sequence-function relationship. Finally, the robustness of the proposed method is assessed by evaluating the performances obtained from four different ensemble methods

Protein-DNA binding sites prediction based on pre-trained protein language model and contrastive learning

Protein-DNA interaction is critical for life activities such as replication, transcription, and splicing. Identifying protein-DNA binding residues is essential for modeling their interaction and downstream studies. However, developing accurate and efficient computational methods for this task remains challenging. Improvements in this area have the potential to drive novel applications in biotechnology and drug design. In this study, we propose a novel approach called CLAPE, which combines a pre-trained protein language model and the contrastive learning method to predict DNA binding residues. We trained the CLAPE-DB model on the protein-DNA binding sites dataset and evaluated the model performance and generalization ability through various experiments. The results showed that the AUC values of the CLAPE-DB model on the two benchmark datasets reached 0.871 and 0.881, respectively, indicating superior performance compared to other existing models. CLAPE-DB showed better generalization ability and was specific to DNA-binding sites. In addition, we trained CLAPE on different protein-ligand binding sites datasets, demonstrating that CLAPE is a general framework for binding sites prediction. To facilitate the scientific community, the benchmark datasets and codes are freely available at https://github.com/YAndrewL/clape

Discriminating between surfaces of peripheral membrane proteins and reference proteins using machine learning algorithms

In biology, the cell membrane is an important component of a cell and usually works as a “fence” to distinguish the inside and outside of a cell. The key role is to protect the cells from being interfered by their surroundings by preventing the molecules that will enter into the cell. However as we know, cells need to keep communicating with their surroundings to acquire nutrition and other necessary molecules in order to stay alive and grow. Due to this reason, membrane proteins are used as molecular carriers to participate the molecular communication and regulate the biological activities. There are two kinds of membrane proteins: integral and peripheral. In this project, we only focus on the latter. Unlike the integral membrane proteins which can go across the whole membrane, peripheral membrane proteins can only attach to the surface of the membrane through various interactions. Because peripheral proteins are also soluble, it is difficult to differentiate them from other kinds of proteins (i.e. non membrane-binding) from sequence or structure. In this project, we will develop a method to predict from its structure wether a protein is membrane-binding protein or not based on two machine learning algorithms: k-nearest neighbors(KNN) and support vector machine(SVM). We use them to train the data and create two models respectively, which will be used to classify new proteins as well as compare their performance. By for example collecting different features of proteins, adjusting the parameters of the algorithms or changing size and structure of the dataset, we can improve the performances of the algorithms as well as predict the protein type more accurately. We also use ROC curve and AUC to present the performance in overview, and cross validation to verify the result. For the problems in this field, several challenges should be considered as well, such as collecting of features, analysis and dealing with the huge variety of data, as well as the choice of machine learning algorithms for a design based on functional requirements, data structure, efficiency and other factors. In this project, we will encounter these challenges and solve them by effective methods.Master's Thesis in InformaticsINF39

Tertiary structure-based prediction of conformational B-cell epitopes through B factors

Motivation: B-cell epitope is a small area on the surface of an antigen that binds to an antibody. Accurately locating epitopes is of critical importance for vaccine development. Compared with wet-lab methods, computational methods have strong potential for efficient and large-scale epitope prediction for antigen candidates at much lower cost. However, it is still not clear which features are good determinants for accurate epitope prediction, leading to the unsatisfactory performance of existing prediction methods. Method and results: We propose a much more accurate B-cell epitope prediction method. Our method uses a new feature B factor (obtained from X-ray crystallography), combined with other basic physicochemical, statistical, evolutionary and structural features of each residue. These basic features are extended by a sequence window and a structure window. All these features are then learned by a two-stage random forest model to identify clusters of antigenic residues and to remove isolated outliers. Tested on a dataset of 55 epitopes from 45 tertiary structures, we prove that our method significantly outperforms all three existing structure-based epitope predictors. Following comprehensive analysis, it is found that features such as B factor, relative accessible surface area and protrusion index play an important role in characterizing B-cell epitopes. Our detailed case studies on an HIV antigen and an influenza antigen confirm that our second stage learning is effective for clustering true antigenic residues and for eliminating self-made prediction errors introduced by the first-stage learning. © 2014 The Author. Published by Oxford University Press. All rights reserved