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1. SubMito-XGBoost: predicting protein submitochondrial localization by fusing multiple feature information and eXtreme gradient boosting

   https://doi.org/10.1093/bioinformatics/btz734  

   Yu, Bin ; Qiu, Wenying ; Chen, Cheng ; Ma, Anjun ; Jiang, Jing ; Zhou, Hongyan ; Ma, Qin ; Hancock, ed., John ( October 2019 , Bioinformatics)

   Mitochondria are an essential organelle in most eukaryotes. They not only play an important role in energy metabolism but also take part in many critical cytopathological processes. Abnormal mitochondria can trigger a series of human diseases, such as Parkinson's disease, multifactor disorder and Type-II diabetes. Protein submitochondrial localization enables the understanding of protein function in studying disease pathogenesis and drug design.

   We proposed a new method, SubMito-XGBoost, for protein submitochondrial localization prediction. Three steps are included: (i) the g-gap dipeptide composition (g-gap DC), pseudo-amino acid composition (PseAAC), auto-correlation function (ACF) and Bi-gram position-specific scoring matrix (Bi-gram PSSM) are employed to extract protein sequence features, (ii) Synthetic Minority Oversampling Technique (SMOTE) is used to balance samples, and the ReliefF algorithm is applied for feature selection and (iii) the obtained feature vectors are fed into XGBoost to predict protein submitochondrial locations. SubMito-XGBoost has obtained satisfactory prediction results by the leave-one-out-cross-validation (LOOCV) compared with existing methods. The prediction accuracies of the SubMito-XGBoost method on the two training datasets M317 and M983 were 97.7% and 98.9%, which are 2.8–12.5% and 3.8–9.9% higher than other methods, respectively. The prediction accuracy of the independent test set M495 was 94.8%, which is significantly better than the existing studies. The proposed method also achieves satisfactory predictive performance on plant and non-plant protein submitochondrial datasets. SubMito-XGBoost also plays an important role in new drug design for the treatment of related diseases.

   The source codes and data are publicly available at https://github.com/QUST-AIBBDRC/SubMito-XGBoost/.

   Supplementary data are available at Bioinformatics online.

    

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2. Predicting protein flexibility with AlphaFold

   https://doi.org/10.1002/prot.26471  

   Ma, Puyi ; Li, Da‐Wei ; Brüschweiler, Rafael ( February 2023 , Proteins: Structure, Function, and Bioinformatics)

   AlphaFold2 has revolutionized protein structure prediction from amino‐acid sequence. In addition to protein structures, high‐resolution dynamics information about various protein regions is important for understanding protein function. Although AlphaFold2 has neither been designed nor trained to predict protein dynamics, it is shown here how the information returned by AlphaFold2 can be used to predict dynamic protein regions at the individual residue level. The approach, which is termed cdsAF2, uses the 3D protein structure returned by AlphaFold2 to predict backbone NMR NHS²order parameters using a local contact model that takes into account the contacts made by each peptide plane along the backbone with its environment. By combining for each residue AlphaFold2's pLDDT confidence score for the structure prediction accuracy with the predictedS²value using the local contact model, an estimator is obtained that semi‐quantitatively captures many of the dynamics features observed in experimental backbone NMR NHS²order parameter profiles. The method is demonstrated for a set nine proteins of different sizes and variable amounts of dynamics and disorder.

    

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3. Towards region-specific propagation of protein functions

   https://doi.org/10.1093/bioinformatics/bty834  

   Koo, Da Chen Emily ; Bonneau, Richard ; Wren, ed., Jonathan ( October 2018 , Bioinformatics)

   Due to the nature of experimental annotation, most protein function prediction methods operate at the protein-level, where functions are assigned to full-length proteins based on overall similarities. However, most proteins function by interacting with other proteins or molecules, and many functional associations should be limited to specific regions rather than the entire protein length. Most domain-centric function prediction methods depend on accurate domain family assignments to infer relationships between domains and functions, with regions that are unassigned to a known domain-family left out of functional evaluation. Given the abundance of residue-level annotations currently available, we present a function prediction methodology that automatically infers function labels of specific protein regions using protein-level annotations and multiple types of region-specific features.

   We apply this method to local features obtained from InterPro, UniProtKB and amino acid sequences and show that this method improves both the accuracy and region-specificity of protein function transfer and prediction. We compare region-level predictive performance of our method against that of a whole-protein baseline method using proteins with structurally verified binding sites and also compare protein-level temporal holdout predictive performances to expand the variety and specificity of GO terms we could evaluate. Our results can also serve as a starting point to categorize GO terms into region-specific and whole-protein terms and select prediction methods for different classes of GO terms.

   The code and features are freely available at: https://github.com/ek1203/rsfp.

   Supplementary data are available at Bioinformatics online.

    

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4. Does Inter-Protein Contact Prediction Benefit from Multi-Modal Data and Auxiliary Tasks?

   Talukder, A. ; Yin, R. ; Sun, Y. ; Shen, Y. ; You, Y. ( January 2022 , Machine Learning in Structural Biology Workshop at the 36th Conference on Neural Information Processing Systems)

   Approaches to in silico prediction of protein structures have been revolutionized by AlphaFold2, while those to predict interfaces between proteins are relatively underdeveloped, owing to the overly complicated yet relatively limited data of protein–protein complexes. In short, proteins are 1D sequences of amino acids folding into 3D structures, and interact to form assemblies to function. We believe that such intricate scenarios are better modeled with additional indicative information that reflects their multi-modality nature and multi-scale functionality. To improve binary prediction of inter-protein residue-residue contacts, we propose to augment input features with multi-modal representations and to synergize the objective with auxiliary predictive tasks. (i) We first progressively add three protein modalities into models: protein sequences, sequences with evolutionary information, and structure-aware intra-protein residue contact maps. We observe that utilizing all data modalities delivers the best prediction precision. Analysis reveals that evolutionary and structural information benefit predictions on the difficult and rigid protein complexes, respectively, assessed by the resemblance to native residue contacts in bound complex structures. (ii) We next introduce three auxiliary tasks via self-supervised pre-training (binary prediction of protein-protein interaction (PPI)) and multi-task learning (prediction of inter-protein residue–residue distances and angles). Although PPI prediction is reported to benefit from predicting intercontacts (as causal interpretations), it is not found vice versa in our study. Similarly, the finer-grained distance and angle predictions did not appear to uniformly improve contact prediction either. This again reflects the high complexity of protein–protein complex data, for which designing and incorporating synergistic auxiliary tasks remains challenging. 

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5. Biomolecular simulation based machine learning models accurately predict sites of tolerability to the unnatural amino acid acridonylalanine

   https://doi.org/10.1038/s41598-021-97965-2  

   Giannakoulias, Sam ; Shringari, Sumant R. ; Ferrie, John J. ; Petersson, E. James ( December 2021 , Scientific Reports)

   Abstract The incorporation of unnatural amino acids (Uaas) has provided an avenue for novel chemistries to be explored in biological systems. However, the successful application of Uaas is often hampered by site-specific impacts on protein yield and solubility. Although previous efforts to identify features which accurately capture these site-specific effects have been unsuccessful, we have developed a set of novel Rosetta Custom Score Functions and alternative Empirical Score Functions that accurately predict the effects of acridon-2-yl-alanine (Acd) incorporation on protein yield and solubility. Acd-containing mutants were simulated in PyRosetta, and machine learning (ML) was performed using either the decomposed values of the Rosetta energy function, or changes in residue contacts and bioinformatics. Using these feature sets, which represent Rosetta score function specific and bioinformatics-derived terms, ML models were trained to predict highly abstract experimental parameters such as mutant protein yield and solubility and displayed robust performance on well-balanced holdouts. Model feature importance analyses demonstrated that terms corresponding to hydrophobic interactions, desolvation, and amino acid angle preferences played a pivotal role in predicting tolerance of mutation to Acd. Overall, this work provides evidence that the application of ML to features extracted from simulated structural models allow for the accurate prediction of diverse and abstract biological phenomena, beyond the predictivity of traditional modeling and simulation approaches. 

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