MULocDeep: A deep-learning framework for protein subcellular and suborganellar localization prediction with residue-level interpretation.
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ABSTRACT:
SUBMITTER: Jiang Y
PROVIDER: S-EPMC8426535 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:AimsTo develop a tool that can annotate subcellular localization of human proteins.BackgroundWith the progression of high throughput human proteomics projects, an enormous amount of protein sequence data has been discovered in the recent past. All these raw sequence data require precise mapping and annotation for their respective biological role and functional attributes. The functional characteristics of protein molecules are highly dependent on the subcellular localization/compartment. Therefore, a fully automated and reliable protein subcellular localization prediction system would be very useful for current proteomic research.ObjectiveTo develop a machine learning-based predictive model that can annotate the subcellular localization of human proteins with high accuracy and precision.MethodsIn this study, we used the PSI-CD-HIT homology criterion and utilized the sequence-based features of protein sequences to develop a powerful subcellular localization predictive model. The dataset used to train the HumDLoc model was extracted from a reliable data source, Uniprot knowledge base, which helps the model to generalize on the unseen dataset.ResultsThe proposed model, HumDLoc, was compared with two of the most widely used techniques: CELLO and DeepLoc, and other machine learning-based tools. The result demonstrated promising predictive performance of HumDLoc model based on various machine learning parameters such as accuracy (≥97.00%), precision (≥0.86), recall (≥0.89), MCC score (≥0.86), ROC curve (0.98 square unit), and precision-recall curve (0.93 square unit).ConclusionIn conclusion, HumDLoc was able to outperform several alternative tools for correctly predicting subcellular localization of human proteins. The HumDLoc has been hosted as a web-based tool at https://bioserver.iiita.ac.in/HumDLoc/.
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Project description:MotivationProtein solubility plays a vital role in pharmaceutical research and production yield. For a given protein, the extent of its solubility can represent the quality of its function, and is ultimately defined by its sequence. Thus, it is imperative to develop novel, highly accurate in silico sequence-based protein solubility predictors. In this work we propose, DeepSol, a novel Deep Learning-based protein solubility predictor. The backbone of our framework is a convolutional neural network that exploits k-mer structure and additional sequence and structural features extracted from the protein sequence.ResultsDeepSol outperformed all known sequence-based state-of-the-art solubility prediction methods and attained an accuracy of 0.77 and Matthew's correlation coefficient of 0.55. The superior prediction accuracy of DeepSol allows to screen for sequences with enhanced production capacity and can more reliably predict solubility of novel proteins.Availability and implementationDeepSol's best performing models and results are publicly deposited at https://doi.org/10.5281/zenodo.1162886 (Khurana and Mall, 2018).Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:BackgroundThe eukaryotic cell has an intricate architecture with compartments and substructures dedicated to particular biological processes. Knowing the subcellular location of proteins not only indicates how bio-processes are organized in different cellular compartments, but also contributes to unravelling the function of individual proteins. Computational localization prediction is possible based on sequence information alone, and has been successfully applied to proteins from virtually all subcellular compartments and all domains of life. However, we realized that current prediction tools do not perform well on partial protein sequences such as those inferred from Expressed Sequence Tag (EST) data, limiting the exploitation of the large and taxonomically most comprehensive body of sequence information from eukaryotes.ResultsWe developed a new predictor, TESTLoc, suited for subcellular localization prediction of proteins based on their partial sequence conceptually translated from ESTs (EST-peptides). Support Vector Machine (SVM) is used as computational method and EST-peptides are represented by different features such as amino acid composition and physicochemical properties. When TESTLoc was applied to the most challenging test case (plant data), it yielded high accuracy (~85%).ConclusionsTESTLoc is a localization prediction tool tailored for EST data. It provides a variety of models for the users to choose from, and is available for download at http://megasun.bch.umontreal.ca/~shenyq/TESTLoc/TESTLoc.html.
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Project description:The subcellular localization of a protein is important for its function and interaction with other molecules, and its mislocalization is linked to numerous diseases. While atlas-scale efforts have been made to profile protein localization across various cell lines, existing datasets only contain limited pairs of proteins and cell lines which do not cover all human proteins. We present a method that uses both protein sequences and cellular landmark images to perform Predictions of Unseen Proteins' Subcellular localization (PUPS), which can generalize to both proteins and cell lines not used for model training. PUPS combines a protein language model and an image inpainting model to utilize both protein sequence and cellular images for protein localization prediction. The protein sequence input enables generalization to unseen proteins and the cellular image input enables cell type specific prediction that captures single-cell variability. PUPS' ability to generalize to unseen proteins and cell lines enables us to assess the variability in protein localization across cell lines as well as across single cells within a cell line and to identify the biological processes associated with the proteins that have variable localization. Experimental validation shows that PUPS can be used to predict protein localization in newly performed experiments outside of the Human Protein Atlas used for training. Collectively, PUPS utilizes both protein sequences and cellular images to predict protein localization in unseen proteins and cell lines with the ability to capture single-cell variability.
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Project description:Proteins are made up of long chain of amino acids that perform a variety of functions in different organisms. The activity of the proteins is determined by the nucleotide sequence of their genes and by its 3D structure. In addition, it is essential for proteins to be destined to their specific locations or compartments to perform their structure and functions. The challenge of computational prediction of subcellular localization of proteins is addressed in various in silico methods. In this review, we reviewed the progress in this field and offered a bird eye view consisting of a comprehensive listing of tools, types of input features explored, machine learning approaches employed, and evaluation matrices applied. We hope the review will be useful for the researchers working in the field of protein localization predictions.
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