Protein Three-dimensional Structure Prediction Research Articles

An interactive visualization tool for educational outreach in protein contact map overlap analysis.

Recent advancements in contact map-based protein three-dimensional (3D) structure prediction have been driven by the evolution of deep learning algorithms. However, the gap in accessible software tools for novices in this domain remains a significant challenge. This study introduces GoFold, a novel, standalone graphical user interface (GUI) designed for beginners to perform contact map overlap (CMO) problems for better template selection. Unlike existing tools that cater more to research needs or assume foundational knowledge, GoFold offers an intuitive, user-friendly platform with comprehensive tutorials. It stands out in its ability to visually represent the CMO problem, allowing users to input proteins in various formats and explore the CMO problem. The educational value of GoFold is demonstrated through benchmarking against the state-of-the-art contact map overlap method, map_align, using two datasets: PSICOV and CAMEO. GoFold exhibits superior performance in terms of TM-score and Z-score metrics across diverse qualities of contact maps and target difficulties. Notably, GoFold runs efficiently on personal computers without any third-party dependencies, thereby making it accessible to the general public for promoting citizen science. The tool is freely available for download for macOS, Linux, and Windows.

Comprehensive Evolutionary Analysis of the SMXL Gene Family in Rosaceae: Further Insights into Its Origin, Expansion, Diversification, and Role in Regulating Pear Branching.

SMXL genes constitute a conserved gene family that is ubiquitous in angiosperms and involved in regulating various plant processes, including branching, leaf elongation, and anthocyanin biosynthesis, but little is known about their molecular functions in pear branching. Here, we performed genome-wide identification and investigation of the SMXL genes in 16 angiosperms and analyzed their phylogenetics, structural features, conserved motifs, and expression patterns. In total, 121 SMXLs genes were identified and were classified into four groups. The number of non-redundant SMXL genes in each species varied from 3 (Amborella trichopoda Baill.) to 18 (Glycine max Merr.) and revealed clear gene expansion events over evolutionary history. All the SMXL genes showed conserved structures, containing no more than two introns. Three-dimensional protein structure prediction revealed distinct structures between but similar structures within groups. A quantitative real-time PCR analysis revealed different expressions of 10 SMXL genes from pear branching induced by fruit-thinning treatment. Overall, our study provides a comprehensive investigation of SMXL genes in the Rosaceae family, especially pear. The results offer a reference for understanding the evolutionary history of SMXL genes and provide excellent candidates for studying fruit tree branching regulation, and in facilitating pear pruning and planting strategies.

Bartletts principal regressive and arbitrary African buffalo optimizatized three-dimensional protein structure prediction

Bartletts principal regressive and arbitrary African buffalo optimizatized three-dimensional protein structure prediction

A Rare Variant in MDH2 (rs111879470) Is Associated with Predisposition to Recurrent Breast Cancer in an Extended High-Risk Pedigree.

A significant fraction of breast cancer recurs, with lethal outcome, but specific genetic variants responsible have yet to be identified. Five cousin pairs with recurrent breast cancer from pedigrees with a statistical excess of recurrent breast cancer were sequenced to identify rare, shared candidate predisposition variants. The candidates were tested for association with breast cancer risk with UKBiobank data. Additional breast cancer cases were assayed for a subset of candidate variants to test for co-segregation. Three-dimensional protein structure prediction methods were used to investigate how the mutation under consideration is predicted to change structural and electrostatic properties in the mutated protein. One hundred and eighty-one rare candidate predisposition variants were shared in at least one cousin pair from a high-risk pedigree. A rare variant in MDH2 was found to segregate with breast-cancer-affected relatives in one extended pedigree. MDH2 is an estrogen-stimulated gene encoding the protein malate dehydrogenase, which catalyzes the reversible oxidation of malate to oxaloacetate. The molecular simulation results strongly suggest that the mutation changes the NAD+ binding pocket electrostatics of MDH2. This small sequencing study, using a powerful approach based on recurrent breast cancer cases from high-risk pedigrees, identified a set of strong candidate variants for inherited predisposition for breast cancer recurrence, including MDH2, which should be pursued in other resources.

Rendering protein mutation movies with MutAmore

BackgroundThe success of AlphaFold2 in reliable protein three-dimensional (3D) structure prediction, assists the move of structural biology toward studies of protein dynamics and mutational impact on structure and function. This transition needs tools that qualitatively assess alternative 3D conformations.ResultsWe introduce MutAmore, a bioinformatics tool that renders individual images of protein 3D structures for, e.g., sequence mutations into a visually intuitive movie format. MutAmore streamlines a pipeline casting single amino-acid variations (SAVs) into a dynamic 3D mutation movie providing a qualitative perspective on the mutational landscape of a protein. By default, the tool first generates all possible variants of the sequence reachable through SAVs (L*19 for proteins with L residues). Next, it predicts the structural conformation for all L*19 variants using state-of-the-art models. Finally, it visualizes the mutation matrix and produces a color-coded 3D animation. Alternatively, users can input other types of variants, e.g., from experimental structures.ConclusionMutAmore samples alternative protein configurations to study the dynamical space accessible from SAVs in the post-AlphaFold2 era of structural biology. As the field shifts towards the exploration of alternative conformations of proteins, MutAmore aids in the understanding of the structural impact of mutations by providing a flexible pipeline for the generation of protein mutation movies using current and future structure prediction models.

RCSB Protein Data Bank: visualizing groups of experimentally determined PDB structures alongside computed structure models of proteins.

Recent advances in Artificial Intelligence and Machine Learning (e.g., AlphaFold, RosettaFold, and ESMFold) enable prediction of three-dimensional (3D) protein structures from amino acid sequences alone at accuracies comparable to lower-resolution experimental methods. These tools have been employed to predict structures across entire proteomes and the results of large-scale metagenomic sequence studies, yielding an exponential increase in available biomolecular 3D structural information. Given the enormous volume of this newly computed biostructure data, there is an urgent need for robust tools to manage, search, cluster, and visualize large collections of structures. Equally important is the capability to efficiently summarize and visualize metadata, biological/biochemical annotations, and structural features, particularly when working with vast numbers of protein structures of both experimental origin from the Protein Data Bank (PDB) and computationally-predicted models. Moreover, researchers require advanced visualization techniques that support interactive exploration of multiple sequences and structural alignments. This paper introduces a suite of tools provided on the RCSB PDB research-focused web portal RCSB. org, tailor-made for efficient management, search, organization, and visualization of this burgeoning corpus of 3D macromolecular structure data.

Prediction of the tetramer protein complex interaction based on CNN and SVM.

Protein-protein interactions play an important role in life activities. The study of protein-protein interactions helps to better understand the mechanism of protein complex interaction, which is crucial for drug design, protein function annotation and three-dimensional structure prediction of protein complexes. In this paper, we study the tetramer protein complex interaction. The research has two parts: The first part is to predict the interaction between chains of the tetramer protein complex. In this part, we proposed a feature map to represent a sample generated by two chains of the tetramer protein complex, and constructed a Convolutional Neural Network (CNN) model to predict the interaction between chains of the tetramer protein complex. The AUC value of testing set is 0.6263, which indicates that our model can be used to predict the interaction between chains of the tetramer protein complex. The second part is to predict the tetramer protein complex interface residue pairs. In this part, we proposed a Support Vector Machine (SVM) ensemble method based on under-sampling and ensemble method to predict the tetramer protein complex interface residue pairs. In the top 10 predictions, when at least one protein-protein interaction interface is correctly predicted, the accuracy of our method is 82.14%. The result shows that our method is effective for the prediction of the tetramer protein complex interface residue pairs.

Novel heterozygous mutation in alpha-2-macroglobulin (A2M) suppressing the binding of amyloid-β (Aβ).

Many studies have suggested that the alpha-2-macroglobulin (A2M) gene may be involved in the pathogenesis of Alzheimer's disease (AD). A2M encoded by the A2M gene can specifically bind to the β-amyloid peptide and prevent fiber formation. The patient in this study had progressive memory loss at the age of 60 years and underwent a series of neuropsychological tests, cranial magnetic resonance imaging (MRI), cerebrospinal fluid (CSF) biomarker analysis, and whole-exome sequencing (WES) to evaluate possible mutations. We used in silico tools and three-dimensional (3D) protein structure prediction to analyze the pathogenicity of the mutation and used a co-immunoprecipitation experiment to study the effect of mutations on amyloid-β (Aβ) binding. Based on neuropsychological tests, cranial MRI, and CSF biomarker analysis, the patient was diagnosed with AD. WES showed that there was a missense mutation in A2M (c.1229A>C, p.N410T). Bioinformatics analysis showed that this mutation was pathogenic. Moreover, 3D protein structure analysis showed that the A2M Asn410 residue was an N-glycosylation site, which was necessary for A2M activation to bind to Aβ. Missense mutations led to the loss of glycosylation at this site, which suppressed the binding of Aβ. The functional experiment also confirmed the prediction: the interaction between A2M and Aβ from the patient's plasma was weakened. Our results demonstrate that this novel A2M p.N410T mutation may have a pathogenic role in AD, by altering the binding interactions between A2M and Aβ.

Improved Protein Real-Valued Distance Prediction Using Deep Residual Dense Network (DRDN).

Three-dimensional protein structure prediction is one of the major challenges in bioinformatics. According to recent research findings, real-valued distance prediction plays a vital role in determining the unique three-dimensional protein structure. This paper proposes a novel methodology involving a deep residual dense network (DRDN) for predicting protein real-valued distance. The features extracted from the given query protein sequence and its corresponding homologous sequences are used for training the model. Multi-aligned homologous sequences for each query protein sequence are retrieved from five different databases using DeepMSA, HHblits, and HITS_PR_HHblits methods. The proposed method yielded outcomes of 3.89, 0.23, 0.45, and 0.63, respectively, corresponding to the evaluation metrics such as Absolute Error, Relative Error, High-accuracy Pairwise Distance Test (PDA), and Pairwise Distance Test (PDT). Further, the contact map is computed based on CASP criteria by converting the predicted real-valued distance, and it is evaluated using the precision metric. It is observed that precision of long-range top L/5 contact prediction on the CASP13 dataset by the proposed method, RaptorX, Zhang, trRosetta, JinboXu & JinLu, and Deepdist are 0.834, 0.657, 0.70, 0.785, 0.786, and 0.812, respectively. Also, Top-L/5 contact prediction on the CASP14 dataset evaluated using average precision resulted in 0.847, 0.707, 0.752, 0.783, 0.792, 0.817, and 0.825 respectively, corresponding to the proposed method, Zhang, RaptorX, trRosetta, Deepdist, JinboXu & JinLu, and Alphafold2.

DeepTracer-ID: De novo protein identification from cryo-EM maps

The recent revolution in cryo-electron microscopy (cryo-EM) has made it possible to determine macromolecular structures directly from cell extracts. However, identifying the correct protein from the cryo-EM map is still challenging and often needs additional sequence information from other techniques, such as tandem mass spectrometry and/or bioinformatics. Here, we present DeepTracer-ID, a server-based approach to identify the candidate protein in a user-provided organism de novo from a cryo-EM map, without the need for additional information. Our method first uses DeepTracer to generate a protein backbone model that best represents the cryo-EM map, and this model is then searched against the library of AlphaFold2 predictions for all proteins in the given organism. This method is highly accurate and robust for high-resolution cryo-EM maps: in all 13 experimental maps tested blindly, DeepTracer-ID identified the correct proteins as the top candidates. Eight of the maps were of known structures, while the other five unpublished maps were validated by prior protein annotation and careful inspection of the model refined into the map. The program also showed promising results for both homomeric and heteromeric protein complexes. This platform is possible because of the recent breakthroughs in large-scale three-dimensional protein structure prediction.

Identification of a new splice-acceptor mutation in HFM1 and functional analysis through molecular docking in nonobstructive azoospermia.

To investigate the genetic cause of nonobstructive azoospermia (NOA). We performed whole exome sequencing (WES) on the proband who had three relatives suffering from NOA. We used a list of candidate genes which have high expression level in testis and their mutations have been reported in NOA. Sanger sequencing verified the identified variant and its structural and functional consequence was evaluated by protein three-dimensional (3D) structure prediction and protein-ligand docking. WES revealed a novel splice-acceptor mutation (c.1832-2A>T) in helicase for meiosis 1 (HFM1) gene, which co-segregated with the NOA in this family. 3D structural models were generated and verified. Molecular docking indicated that the c.1832-2A>T mutation affects not only the ADP binding residues but also the hydrogen bond interactions. The ADP binding site will be lost in the mutant protein, potentially causing defective crossover and synapsis. We report that the c.1832-2A>T mutation is the likely cause of NOA in the family studied. Regarding that many reported NOA genes are involved in the formation of crossovers and synapsis and have critical roles in the production of germ cells, we suggest that such genes should be considered for screening of infertility among large cohorts of infertile individuals.

Structural Models for Roseolovirus U20 And U21: Non-Classical MHC-I Like Proteins From HHV-6A, HHV-6B, and HHV-7.

Human roseolovirus U20 and U21 are type I membrane glycoproteins that have been implicated in immune evasion by interfering with recognition of classical and non-classical MHC proteins. U20 and U21 are predicted to be type I glycoproteins with extracytosolic immunoglobulin-like domains, but detailed structural information is lacking. AlphaFold and RoseTTAfold are next generation machine-learning-based prediction engines that recently have revolutionized the field of computational three-dimensional protein structure prediction. Here, we review the structural biology of viral immunoevasins and the current status of computational structure prediction algorithms. We use these computational tools to generate structural models for U20 and U21 proteins, which are predicted to adopt MHC-Ia-like folds with closed MHC platforms and immunoglobulin-like domains. We evaluate these structural models and place them within current understanding of the structural basis for viral immune evasion of T cell and natural killer cell recognition.

Analytical tools in protein structure determination

Three-dimensional protein structure prediction from amino acid sequence has been a thought-provoking task for decades, but it of pivotal importance as it provides a better understanding of its function. In recent years, the methods for prediction of protein structures have advanced considerably. Computational techniques and increase in protein sequence and structure databases have influence the laborious protein structure determination process. Still there is no single method which can predict all the protein structures. In this review, we describe the four stages of protein structure determination. We have also explored the currenttechniques used to uncover the protein structure and highpoint best suitable method for a given protein.

DiSBPred: A machine learning based approach for disulfide bond prediction

The protein disulfide bond is a covalent bond that forms during post-translational modification by the oxidation of a pair of cysteines. In protein, the disulfide bond is the most frequent covalent link between amino acids after the peptide bond. It plays a significant role in three-dimensional (3D) ab initio protein structure prediction (aiPSP), stabilizing protein conformation, post-translational modification, and protein folding. In aiPSP, the location of disulfide bonds can strongly reduce the conformational space searching by imposing geometrical constraints. Existing experimental techniques for the determination of disulfide bonds are time-consuming and expensive. Thus, developing sequence-based computational methods for disulfide bond prediction becomes indispensable. This study proposed a stacking-based machine learning approach for disulfide bond prediction (diSBPred). Various useful sequence and structure-based features are extracted for effective training, including conservation profile, residue solvent accessibility, torsion angle flexibility, disorder probability, a sequential distance between cysteines, and more. The prediction of disulfide bonds is carried out in two stages: first, individual cysteines are predicted as either bonding or non-bonding; second, the cysteine-pairs are predicted as either bonding or non-bonding by including the results from cysteine bonding prediction as a feature.The examination of the relevance of the features employed in this study and the features utilized in the existing nearest neighbor algorithm (NNA) method shows that the features used in this study improve about 7.39 % in jackknife validation balanced accuracy. Moreover, for individual cysteine bonding prediction and cysteine-pair bonding prediction, diSBPred provides a 10-fold cross-validation balanced accuracy of 82.29 % and 94.20 %, respectively. Altogether, our predictor achieves an improvement of 43.25 % based on balanced accuracy compared to the existing NNA based approach. Thus, diSBPred can be utilized to annotate the cysteine bonding residues of protein sequences whose structures are unknown as well as improve the accuracy of the aiPSP method, which can further aid in experimental studies of the disulfide bond and structure determination.

Protein secondary structure prediction based on integration of CNN and LSTM model

Protein structure prediction is an important issue in computational biology, and protein secondary structure prediction is the basis for protein three-dimensional structure prediction. A protein secondary structure prediction method based on convolutional neural networks (CNN) and Long Short-Term Memory (LSTM) is proposed in this paper. The architecture of CNN has two convolutional layers, one max-pooling layer and one ReLU activation layer. The feature maps extracted from second convolutional layer are used to feed to softmax classifier, and the first probability output is obtained. The LSTM model has a sequence layer and a last layer. The feature is extracted from last layer and input to random forest classifier to get the second probability output. The two probabilistic outputs are weighted and integrated to obtain the prediction model EN-CSLR in this paper. Based on the advantages of integration of the two models, cross-validation experiments are performed on the 25pdb dataset, and Q3 reaches 80.18%, which is higher than using only one model. The experimental results show that the features extracted from CNN and LSTM models can effectively improve the accuracy of protein secondary structure prediction.

A multi-population memetic algorithm for the 3-D protein structure prediction problem

In this paper, we present a knowledge-based memetic algorithm to tackle the three-dimensional protein structure prediction problem without the explicit use of experimentally determined protein structures' templates. Our algorithm proposal was divided into two main prediction steps: (i) solutions sampling and initialization; and (ii) structural models’ optimization coming from the previous stage. The first step generates and classifies several structural models for a given target protein, through the Angle Probability List strategy, to identify distinct structural patterns and consider reasonable solutions in the memetic algorithm initialization. The Angle Probability List takes advantage of structural knowledge stored in the Protein Data Bank to reduce the size and, consequently, the conformational search space complexity. The second step of the method consists in the optimization of the structures generated in the first stage by the proposed memetic algorithm. It uses a tree-based population where each node can be seen as an independent subpopulation that interacts with each other over global search operations, aiming at knowledge sharing, population diversity, and better exploration of the multimodal search space. The method also encompasses ad hoc global search operators, whose objective is to increase the method exploration ability focusing on specific characteristics of the protein structure prediction problem, combined with the artificial bee colony algorithm used as an exploitation technique applied to each node of the tree. The proposed algorithm was tested on a set of 24 amino acid sequences, as well as compared to the reference method in the protein structure prediction area, the method of Rosetta. The obtained results show the ability of our method to predict three-dimensional protein structures with similar folding to the experimentally determined ones, regarding the structural metrics Root-Mean-Square Deviation and Global Distance Total Score Test. We also show that our method was able to reach comparable results to Rosetta, and in some cases, it outperformed Rosetta, corroborating the effectiveness of our proposal.

Protein Structure Prediction Based on Improved Genetic Algorithm

The prediction of protein three-dimensional structure from amino acid sequence has been a challenge problem in bioinformatics, owing to the many potential applications for robust protein structure prediction methods. Protein structure prediction is essential to bioscience, and its research results are important for other research areas. Methods for the prediction an才d design of protein structures have advanced dramatically. The prediction of protein structure based on average hydrophobic values is discussed and an improved genetic algorithm is proposed to solve the optimization problem of hydrophobic protein structure prediction. An adjustment operator is designed with the average hydrophobic value to prevent the overlapping of amino acid positions. Finally, some numerical experiments are conducted to verify the feasibility and effectiveness of the proposed algorithm by comparing with the traditional HNN algorithm.

AmoebaContact and GDFold as a pipeline for rapid de novo protein structure prediction

Predicting the structures of proteins from amino acid sequences is of great importance. Recently, the accuracy of de novo protein structure prediction has been substantially improved when assisted by information about the contact between residues, which is also predictable from the sequence. Here, we present a novel pipeline for rapid protein structure prediction, which consists of a residue contact predictor, AmoebaContact, and a contact-assisted folder, GDFold. Unlike mainstream contact predictors that utilize simple, regularized neural networks, AmoebaContact adopts a set of network architectures that are optimized for contact prediction through automatic searching, and it predicts contacts at a series of cutoffs. Unlike conventional contact-assisted folders that only use top-scored contact pairs, GDFold considers all residue pairs from the prediction results of AmoebaContact in a differentiable loss function and optimizes atom coordinates using the gradient descent algorithm. The combination of AmoebaContact and GDFold allows quick modelling of the protein structure with acceptable model quality. Predicting the structure of proteins from amino acid sequences is a hard problem. Convolutional neural networks can learn to predict a map of distances between amino acid residues that can be turned into a three-dimensional structure. With a combination of approaches, including an evolutionary technique to find the best neural network architecture and a tool to find the atom coordinates in the folded structure, a pipeline for rapid prediction of three-dimensional protein structures is demonstrated.

Advances in protein structure prediction and design.

The prediction of protein three-dimensional structure from amino acid sequence has been a grand challenge problem in computational biophysics for decades, owing to its intrinsic scientific interest and also to the many potential applications for robust protein structure prediction algorithms, from genome interpretation to protein function prediction. More recently, the inverse problem - designing an amino acid sequence that will fold into a specified three-dimensional structure - has attracted growing attention as a potential route to the rational engineering of proteins with functions useful in biotechnology and medicine. Methods for the prediction and design of protein structures have advanced dramatically in the past decade. Increases in computing power and the rapid growth in protein sequence and structure databases have fuelled the development of new data-intensive and computationally demanding approaches for structure prediction. New algorithms for designing protein folds and protein-protein interfaces have been used to engineer novel high-order assemblies and to design from scratch fluorescent proteins with novel or enhanced properties, as well as signalling proteins with therapeutic potential. In this Review, we describe current approaches for protein structure prediction and design and highlight a selection of the successful applications they have enabled.

Molecular cloning and functional characterization of apple U-box E3 ubiquitin ligase gene MdPUB29 reveals its involvement in salt tolerance

An E3 ubiquitin ligase gene (Genbank accession no.: MD01G1010900) was cloned from the Royal Gala apple genome (Malusxdomestica Borkh.). Sequence analysis showed that the length of the MdPUB29 gene was 1 275 bp, encoding 424 amino acids. Phylogenetic tree analysis indicated that the apple E3 ubiquitin ligase exhibited the greatest sequence similarity to Pyrus×bretschneideri. The predicted protein structural domain of MdPUB29 showed that it contained a U-box domain. qRT-PCR analysis showed that MdPUB29 was expressed widely in different tissues of the Royal Gala apple species, and was highly expressed in the root, while the expression of MdPUB29 was significantly inhibited by exogenous NaCl. Immunoblotting assays revealed that MdPUB29 protein abundance in tissue cultures of the Royal Gala apple accumulated under NaCl stress conditions. Three-dimensional protein structure prediction indicated that MdPUB29 was highly homologous with AtPUB29. The growing potential of MdPUB29-expressing apple calli and Arabidopsis were much stronger than that of the control under salt stress conditions, suggesting that MdPUB29 may positively regulate salt tolerance.