Discovering the Ultimate Limits of Protein Secondary Structure Prediction

PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network

BACKGROUND

Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of orphans and other fewer evolution information proteins. (2) The other bottleneck is the method of predicting single-sequence-based proteins mainly focuses on selecting protein sequence features and tuning the neural network architecture, However, while the deeper neural networks improve prediction accuracy, there is still the problem of increasing the computational burden. Compared with other neural networks in the field of protein prediction, the graph neural network has the following advantages: due to the advantage of revealing the topology structure via graph neural network and being able to take advantage of the hierarchical structure and local connectivity of graph neural networks has certain advantages in capturing the features of different levels of abstraction in protein molecules. When using protein sequence and structure information for joint training, the dependencies between the two kinds of information can be better captured. And it can process protein molecular structures of different lengths and shapes, while traditional neural networks need to convert proteins into fixed-size vectors or matrices for processing.

RESULTS

Here, we propose a single-sequence-based protein contact map predictor PCP-GC-LM, with dual-level graph neural networks and convolution networks. Our method performs better with other single-sequence-based predictors in different independent tests. In addition, to verify the validity of our method against complex protein structures, we will also compare it with other methods in two homodimers protein test sets (DeepHomo test dataset and CASP-CAPRI target dataset). Furthermore, we also perform ablation experiments to demonstrate the necessity of a dual graph network. In all, our framework presents new modules to accurately predict inter-chain contact maps in protein and it's also useful to analyze interactions in other types of protein complexes.

Toward Robust Self-Training Paradigm for Molecular Prediction Tasks

Molecular prediction tasks normally demand a series of professional experiments to label the target molecule, which suffers from the limited labeled data problem. One of the semisupervised learning paradigms, known as self-training, utilizes both labeled and unlabeled data. Specifically, a teacher model is trained using labeled data and produces pseudo labels for unlabeled data. These labeled and pseudo-labeled data are then jointly used to train a student model. However, the pseudo labels generated from the teacher model are generally not sufficiently accurate. Thus, we propose a robust self-training strategy by exploring robust loss function to handle such noisy labels in two paradigms, that is, generic and adaptive. We have conducted experiments on three molecular biology prediction tasks with four backbone models to gradually evaluate the performance of the proposed robust self-training strategy. The results demonstrate that the proposed method enhances prediction performance across all tasks, notably within molecular regression tasks, where there has been an average enhancement of 41.5%. Furthermore, the visualization analysis confirms the superiority of our method. Our proposed robust self-training is a simple yet effective strategy that efficiently improves molecular biology prediction performance. It tackles the labeled data insufficient issue in molecular biology by taking advantage of both labeled and unlabeled data. Moreover, it can be easily embedded with any prediction task, which serves as a universal approach for the bioinformatics community.

Effects of different signaling pathways on odontogenic differentiation of dental pulp stem cells: a review

Dental pulp stem cells (DPSCs) are a type of mesenchymal stem cells that can differentiate into odontoblast-like cells and protect the pulp. The differentiation of DPSCs can be influenced by biomaterials or growth factors that activate different signaling pathways in vitro or in vivo. In this review, we summarized six major pathways involved in the odontogenic differentiation of DPSCs, Wnt signaling pathways, Smad signaling pathways, MAPK signaling pathways, NF-kB signaling pathways, PI3K/AKT/mTOR signaling pathways, and Notch signaling pathways. Various factors can influence the odontogenic differentiation of DPSCs through one or more signaling pathways. By understanding the interactions between these signaling pathways, we can expand our knowledge of the mechanisms underlying the regeneration of the pulp-dentin complex.

Deep learning for protein secondary structure prediction: Pre and post-AlphaFold

This paper aims to provide a comprehensive review of the trends and challenges of deep neural networks for protein secondary structure prediction (PSSP). In recent years, deep neural networks have become the primary method for protein secondary structure prediction. Previous studies showed that deep neural networks had uplifted the accuracy of three-state secondary structure prediction to more than 80%. Favored deep learning methods, such as convolutional neural networks, recurrent neural networks, inception networks, and graph neural networks, have been implemented in protein secondary structure prediction. Methods adapted from natural language processing (NLP) and computer vision are also employed, including attention mechanism, ResNet, and U-shape networks. In the post-AlphaFold era, PSSP studies focus on different objectives, such as enhancing the quality of evolutionary information and exploiting protein language models as the PSSP input. The recent trend to utilize pre-trained language models as input features for secondary structure prediction provides a new direction for PSSP studies. Moreover, the state-of-the-art accuracy achieved by previous PSSP models is still below its theoretical limit. There are still rooms for improvement to be made in the field.

Propensities of Some Amino Acid Pairings in α-Helices Vary with Length

The results of secondary structure prediction methods are widely used in applications in biotechnology and bioinformatics. However, the accuracy limit of these methods could be improved up to 92%. One approach to achieve this goal is to harvest information from the primary structure of the peptide. This study aims to contribute to this goal by investigating the variations in propensity of amino acid pairings to α-helices in globular proteins depending on helix length. (n):(n + 4) residue pairings were determined using a comprehensive peptide data set according to backbone hydrogen bond criterion which states that backbone hydrogen bond is the dominant driving force of protein folding. Helix length is limited to 13 to 26 residues. Findings of this study show that propensities of ALA:GLY and GLY:GLU pairings to α-helix in globular protein increase with increasing helix length but of ALA:ALA and ALA:VAL decrease. While the frequencies of ILE:ALA, LEU:ALA, LEU:GLN, LEU:GLU, LEU:LEU, MET:ILE and VAL:LEU pairings remain roughly constant with length, the 25 residue pairings have varying propensities in narrow helix lengths. The remaining pairings have no prominent propensity to α-helices.