Problems In Structural Biology Research Articles

Molecular dynamics and small-angle x-ray scattering: a comparison computational and experimental approaches to studying the structure of biological complexes

The results of studying DNA-protein complexes using two independent structural methods – molecular dynamics (MD) and small-angle X-ray scattering (SAXS) – are compared. MD is a computational method that allows visualization of macromolecule behavior in real environmental conditions based on the laws of physics but suffers from numerous simplifications. SAXS is an X-ray method that allows the reconstruction of the three-dimensional structure of an object in solution based on the one-dimensional profile of small-angle scattering, which presents the problem of ambiguity in solving inverse problems. The use of structural characteristics of complexes obtained by the SAXS method for validating 3D structural models obtained in MD experiments has significantly reduced the ambivalence of theoretical predictions and demonstrated the effectiveness of combining MD and SAXS methods for solving structural biology problems.

Insights into AlphaFold's breakthrough in neurodegenerative diseases.

Neurodegenerative diseases (ND) are disorders of the central nervous system (CNS) characterized by impairment in neurons' functions, and complete loss, leading to memory loss, and difficulty in learning, language, and movement processes. The most common among these NDs are Alzheimer's disease (AD) and Parkinson's disease (PD), although several other disorders also exist. These are frontotemporal dementia (FTD), amyotrophic lateral syndrome (ALS), Huntington's disease (HD), and others; the major pathological hallmark of NDs is the proteinopathies, either of amyloid-β (Aβ), tauopathies, or synucleinopathies. Aggregation of proteins that do not undergo normal configuration, either due to mutations or through some disturbance in cellular pathway contributes to the diseases. Artificial Intelligence (AI) and deep learning (DL) have proven to be successful in the diagnosis and treatment of various congenital diseases. DL approaches like AlphaFold (AF) are a major leap towards success in CNS disorders. This 3D protein geometry modeling algorithm developed by DeepMind has the potential to revolutionize biology. AF has the potential to predict 3D-protein confirmation at an accuracy level comparable to experimentally predicted one, with the additional advantage of precisely estimating protein interactions. This breakthrough will be beneficial to identify diseases' advancement and the disturbance of signaling pathways stimulating impaired functions of proteins. Though AlphaFold has solved a major problem in structural biology, it cannot predict membrane proteins-a beneficial approach for drug designing.

DSSGNN-PPI: A Protein–Protein Interactions prediction model based on Double Structure and Sequence graph neural networks

The process of experimentally confirming complex interaction networks among proteins is time-consuming and laborious. This study aims to address Protein–Protein Interactions (PPIs) prediction based on graph neural networks (GNN). A novel multilevel prediction model for PPIs named DSSGNN-PPI (Double Structure and Sequence GNN for PPIs) is designed. Initially, a distance graph between amino acid residues is constructed. Subsequently, the distance graph is fed into an underlying graph attention network module. This enables us to efficiently learn vector representations that encode the three-dimensional structure of proteins and simultaneously aggregate key local patterns and overall topological information to obtain graph embedding that adequately represent local and global structural features. In addition, the embedding representations that reflect sequence properties are obtained. Two features are fused to construct high-level protein complex networks, which are fed into the designed gated graph attention network to extract complex topological patterns. By combining heterogeneous multi-source information from downstream structure graph and upstream sequence models, the understanding of PPIs is comprehensively enhanced. A series of evaluation results validate the remarkable effectiveness of DSSGNN-PPI framework in enhancing the prediction of multi-type interactions among proteins. The multilevel representation learning and information fusion strategies provide a new effective solution paradigm for structural biology problems. The source code for DSSGNN-PPI has been hosted on GitHub and is available at https://github.com/cstudy1/DSSGNN-PPI.

ICARUS: flexible protein structural alignment based on Protein Units.

Alignment of protein structures is a major problem in structural biology. The first approach commonly used is to consider proteins as rigid bodies. However, alignment of protein structures can be very complex due to conformational variability, or complex evolutionary relationships between proteins such as insertions, circular permutations or repetitions. In such cases, introducing flexibility becomes useful for two reasons: (i) it can help compare two protein chains which adopted two different conformational states, such as due to proteins/ligands interaction or post-translational modifications, and (ii) it aids in the identification of conserved regions in proteins that may have distant evolutionary relationships. We propose ICARUS, a new approach for flexible structural alignment based on identification of Protein Units, evolutionarily preserved structural descriptors of intermediate size, between secondary structures and domains. ICARUS significantly outperforms reference methods on a dataset of very difficult structural alignments. Code is freely available online at https://github.com/DSIMB/ICARUS.

The identification and quantification of metal atoms in proteins using microPIXE: A critical evaluation

Around one third of all known protein molecules contain a small number of metal atoms which are often crucial to the biological function of the molecule. Identifying and quantifying these is not possible using commonly available analysis tools and there is a growing awareness that a majority of the metal atoms in the over 38,000 metalloprotein structures deposited in the Protein Data Bank are misidentified, with major consequences for the interpretation of protein function and mechanism. In the 1990s the authors developed a method for the identification and quantification of metals in liquid and crystalline protein samples by combining microbeam Proton Induced X-ray Emission and Rutherford backscattering. This has been used on overexpressed and purified proteins and has addressed many critical metal identification problems in structural biology. The capability has recently been extended to provide high throughput capability.In this paper we give an overview of the methodology and provide a critical evaluation of accuracy and detection limit and provide guidance on the optimisation of sample preparation and analysis conditions for these measurements. The discussion will be illustrated using some recent results illustrating the power of this method to answer biological questions.

Possibilities of Studying Biological Objects on a Pulsed Reactor

Small-angle scattering makes it possible to solve structural biology problems without specific sample preparation, which is typical for methods such as X-ray diffraction of protein crystals or cryo-electron microscopy of proteins. In our review, it is shown how to use small-angle scattering to address biological problems. The use of small-angle scattering is suggested for applications as a tool to control the quality of the assembly of proteins and protein complexes and to test the identity of the structural organization of biological objects in the native state and in prepared samples before measurements by X-ray diffraction or cryo-electron microscopy. This work demonstrates the possibilities of the small-angle neutron scattering spectrometer YuMO based on the IBR-2 pulsed reactor (Laboratory of neutron physics, Joint Institute for Nuclear Research, Dubna, Russia) to solve a whole array of problems, with an eye toward applying these in biophysics, structural biology, and biotechnology. This review presents and discusses the main findings of the studies of various biological systems obtained by using the setup small-angle scattering of neutrons YuMO. The possibilities of development of structural biology methods with the help of small-angle scattering, including protein crystallization, are shown.

An analysis of protein language model embeddings for fold prediction.

The identification of the protein fold class is a challenging problem in structural biology. Recent computational methods for fold prediction leverage deep learning techniques to extract protein fold-representative embeddings mainly using evolutionary information in the form of multiple sequence alignment (MSA) as input source. In contrast, protein language models (LM) have reshaped the field thanks to their ability to learn efficient protein representations (protein-LM embeddings) from purely sequential information in a self-supervised manner. In this paper, we analyze a framework for protein fold prediction using pre-trained protein-LM embeddings as input to several fine-tuning neural network models, which are supervisedly trained with fold labels. In particular, we compare the performance of six protein-LM embeddings: the long short-term memory-based UniRep and SeqVec, and the transformer-based ESM-1b, ESM-MSA, ProtBERT and ProtT5; as well as three neural networks: Multi-Layer Perceptron, ResCNN-BGRU (RBG) and Light-Attention (LAT). We separately evaluated the pairwise fold recognition (PFR) and direct fold classification (DFC) tasks on well-known benchmark datasets. The results indicate that the combination of transformer-based embeddings, particularly those obtained at amino acid level, with the RBG and LAT fine-tuning models performs remarkably well in both tasks. To further increase prediction accuracy, we propose several ensemble strategies for PFR and DFC, which provide a significant performance boost over the current state-of-the-art results. All this suggests that moving from traditional protein representations to protein-LM embeddings is a very promising approach to protein fold-related tasks.

TMQuery: a database of precomputed template modeling scores for assessment of protein structural similarity.

Comparisons of protein structures are critical for developing novel protein designs, annotating protein functions and predicting protein structure. The template modeling score (TM-score) is a widely used but computationally expensive measure of protein similarity that is applicable to a wide variety of structural biology problems. We introduce TMQuery-a continuously updated database containing over eight billion pre-computed TM-score values for every pair of proteins in the Protein Data Bank, allowing researchers to quickly query and download TM-scores via a web interface. Publicly available at https://tmquery.gsk.com/.

In search of new analogues as anti-fungal agents

Development of new Clotrimazole derivatives was studied here. Automated prediction of protein-small molecule interactions is one of the most challenging problems in structural biology as well as in medicinal chemistry. Many biological studies, both in academia and in industry, may benefit from credible high-accuracy interaction predictions. The molecular interactions with target protein have been discussed. A target based designing of new derivatives for future drug development presented, which can be further informative and marketed if their biological significance stand out better than the marketed one. The report concludes with a description of the initial efforts to prepare synthesized compounds to recognize as better antifungal agents than the regular prescribed drugs in market. The in-silico drug designing leading to initial successes are described along with future directions.

Recent technological developments for native mass spectrometry

Native mass spectrometry (MS), the analysis of proteins and protein complexes from solutions that stabilize native solution structures, is a rapidly expanding area. There is strong evidence supporting the retention of proteins' native folds in the absence of solvent under the experimental timescales of MS experiments. Therefore, instrumentation has been developed to use gas-phase native-like protein ions to exploit the speed, sensitivity, and selectivity of mass spectrometry approaches to solve emerging problems in structural biology. This article reviews some of the recent advances and applications in gas-phase instrumentation for structural proteomics.

Geometric deep learning of RNA structure.

RNA molecules adopt three-dimensional structures that are critical to their function and of interest in drug discovery. Few RNA structures are known, however, and predicting them computationally has proven challenging. We introduce a machine learning approach that enables identification of accurate structural models without assumptions about their defining characteristics, despite being trained with only 18 known RNA structures. The resulting scoring function, the Atomic Rotationally Equivariant Scorer (ARES), substantially outperforms previous methods and consistently produces the best results in community-wide blind RNA structure prediction challenges. By learning effectively even from a small amount of data, our approach overcomes a major limitation of standard deep neural networks. Because it uses only atomic coordinates as inputs and incorporates no RNA-specific information, this approach is applicable to diverse problems in structural biology, chemistry, materials science, and beyond.

Integrating Multimeric Threading With High-throughput Experiments for Structural Interactome of Escherichia coli

Genome-wide protein–protein interaction (PPI) determination remains a significant unsolved problem in structural biology. The difficulty is twofold since high-throughput experiments (HTEs) have often a relatively high false-positive rate in assigning PPIs, and PPI quaternary structures are more difficult to solve than tertiary structures using traditional structural biology techniques. We proposed a uniform pipeline, Threpp, to address both problems. Starting from a pair of monomer sequences, Threpp first threads both sequences through a complex structure library, where the alignment score is combined with HTE data using a naïve Bayesian classifier model to predict the likelihood of two chains to interact with each other. Next, quaternary complex structures of the identified PPIs are constructed by reassembling monomeric alignments with dimeric threading frameworks through interface-specific structural alignments. The pipeline was applied to the Escherichia coli genome and created 35,125 confident PPIs which is 4.5-fold higher than HTE alone. Graphic analyses of the PPI networks show a scale-free cluster size distribution, consistent with previous studies, which was found critical to the robustness of genome evolution and the centrality of functionally important proteins that are essential to E. coli survival. Furthermore, complex structure models were constructed for all predicted E. coli PPIs based on the quaternary threading alignments, where 6771 of them were found to have a high confidence score that corresponds to the correct fold of the complexes with a TM-score >0.5, and 39 showed a close consistency with the later released experimental structures with an average TM-score = 0.73. These results demonstrated the significant usefulness of threading-based homologous modeling in both genome-wide PPI network detection and complex structural construction.

Inline Liquid Chromatography-Fast Photochemical Oxidation of Proteins for Targeted Structural Analysis of Conformationally Heterogeneous Mixtures.

Structural analysis of proteins in a conformationally heterogeneous mixture has long been a difficult problem in structural biology. In structural analysis by covalent labeling mass spectrometry, conformational heterogeneity results in data reflecting a weighted average of all conformers, complicating data analysis and potentially causing misinterpretation of results. Here, we describe a method coupling size-exclusion chromatography (SEC) with hydroxyl radical protein footprinting using inline fast photochemical oxidation of proteins (FPOP). Using a controlled synthetic mixture of holomyoglobin and apomyoglobin, we validate that we can achieve accurate footprints of each conformer using LC-FPOP when compared to offline FPOP of each pure conformer. We then applied LC-FPOP to analyze the adalimumab heat-shock aggregation process. We found that the LC-FPOP footprint of unaggregated adalimumab was consistent with a previously published footprint of the native IgG. The LC-FPOP footprint of the aggregation product indicated that heat-shock aggregation primarily protected the hinge region, suggesting that this region is involved with the heat-shock aggregation process of this molecule. LC-FPOP offers a new method to probe dynamic conformationally heterogeneous mixtures that can be separated by SEC such as biopharmaceutical aggregates and to obtain accurate information on the topography of each conformer.

Geometric Deep Learning on Biomolecular Structure

Recent years have seen a dramatic increase in the number of available biomolecular structures, in large part due to advances in experimental methods such as X-ray crystallography and cryo-electron microscopy. These data in turn enable large-scale machine learning approaches that can computationally predict important biological quantities. In this work, we present a new deep learning technique that achieves unprecedented performance on a number of important structural biology problems ranging from the prediction of protein interfaces to assessment of the quality of RNA structural models.

Fourier transformation in spherical systems as a tool of structural biology

Applications of the most common adaptation of Fourier analysis in spherical coordinate systems used to solve a number of problems in structural biology, namely, flat wave decomposition (flat waves are represented as spherical functions decomposition), are herein considered. Arguments in favor of this decomposition are compared with other decompositions in superposition of special functions. A more general justification for the correctness of this decomposition is obtained than that existing today. A method for representing groups of atoms in the form of a Fourier object is proposed. It is also considered what opportunities give such a representation. The prospects for the application of Fourier analysis in structural biophysics are discussed.

The evolution of contact prediction: evidence that contact selection in statistical contact prediction is changing.

Over the last few years, the field of protein structure prediction has been transformed by increasingly accurate contact prediction software. These methods are based on the detection of coevolutionary relationships between residues from multiple sequence alignments (MSAs). However, despite speculation, there is little evidence of a link between contact prediction and the physico-chemical interactions which drive amino-acid coevolution. Furthermore, existing protocols predict only a fraction of all protein contacts and it is not clear why some contacts are favoured over others. Using a dataset of 863 protein domains, we assessed the physico-chemical interactions of contacts predicted by CCMpred, MetaPSICOV and DNCON2, as examples of direct coupling analysis, meta-prediction and deep learning. We considered correctly predicted contacts and compared their properties against the protein contacts that were not predicted. Predicted contacts tend to form more bonds than non-predicted contacts, which suggests these contacts may be more important than contacts that were not predicted. Comparing the contacts predicted by each method, we found that metaPSICOV and DNCON2 favour accuracy, whereas CCMPred detects contacts with more bonds. This suggests that the push for higher accuracy may lead to a loss of physico-chemically important contacts. These results underscore the connection between protein physico-chemistry and the coevolutionary couplings that can be derived from MSAs. This relationship is likely to be relevant to protein structure prediction and functional analysis of protein structure and may be key to understanding their utility for different problems in structural biology. We use publicly available databases. Our code is available for download at https://opig.stats.ox.ac.uk/. Supplementary information is available at Bioinformatics online.

Solid-Phase Chemical Synthesis of Stable Isotope-Labeled RNA to Aid Structure and Dynamics Studies by NMR Spectroscopy

RNA structure and dynamic studies by NMR spectroscopy suffer from chemical shift overlap and line broadening, both of which become worse as RNA size increases. Incorporation of stable isotope labels into RNA has provided several solutions to these limitations. Nevertheless, the only method to circumvent the problem of spectral overlap completely is the solid-phase chemical synthesis of RNA with labeled RNA phosphoramidites. In this review, we summarize the practical aspects of this methodology for NMR spectroscopy studies of RNA. These types of investigations lie at the intersection of chemistry and biophysics and highlight the need for collaborative efforts to tackle the integrative structural biology problems that exist in the RNA world. Finally, examples of RNA structure and dynamic studies using labeled phosphoramidites are highlighted.

A Novel Way of Comparing Protein 3D Structure Using Graph Partitioning Approach.

Alignment and comparison of protein 3D structures is an important and fundamental task in structural biology to study evolutionary, functional and structural relatedness among proteins. Since two decades, the research on protein structure alignment has been taken up on priority and numbers of research articles are being published. There are incremental advances over previous efforts, and still these methods continue to improve over the time and still this is an open problem in structural biology. A novel methodology has been developed for comparing protein 3D structure by employing conversion of pair of protein 3D structures into 2D graphs (undirected weighted graph), partitioning of 2D graphs into sub-graphs, matching sub-graphs with main graphs and finally these sub-graphs matches calculates similarity between the pair of proteins. The proposed method has been implemented in MATLAB and R Package. The performance of the developed methodology is tested with four existing best methods such as CE, jFATCAT, TM_Align and Dali on 100 proteins benchmark dataset with SCOP database. The proposed method is efficient in terms of time complexity, accuracy, grouping of proteins in relevant structural groups and provides additional information towards non-bonded interactions and sub-graphs indicates the dominance of secondary structure.

Guest Editors’ Introduction to the Special Section on Bioinformatics Research and Applications

This special section includes a selection of papers presented at the Eighth International Symposium on Bioinformatics Research and Application (ISBRA), which was held in Dallas, Texas, on 21-23 May 2012. The ISBRA symposium provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications. In 2012, 66 papers were submitted in response to the call for papers, out of which 26 papers appeared in the ISBRA proceedings published as volume 7292 of Springer Verlag's Lecture Notes in Bioinformatics series. Extended versions of nine symposium papers were invited and accepted for publication in this special section following a rigorous review process. The selected papers cover a broad range of bioinformatics topics, including biological networks, computational complexity of problems in structural biology and genomics, and phylogenetic inference and analysis. Herein, we briefly introduce each of them.

Visualization of biological macromolecules at near-atomic resolution: cryo-electron microscopy comes of age.

Structural biology is going through a revolution as a result of transformational advances in the field of cryo-electron microscopy (cryo-EM) driven by the development of direct electron detectors and ultrastable electron microscopes. High-resolution cryo-EM images of isolated biomolecules (single particles) suspended in a thin layer of vitrified buffer are subjected to powerful image-processing algorithms, enabling near-atomic resolution structures to be determined in unprecedented numbers. Prior to these advances, electron crystallography of two-dimensional crystals and helical assemblies of proteins had established the feasibility of atomic resolution structure determination using cryo-EM. Atomic resolution single-particle analysis, without the need for crystals, now promises to resolve problems in structural biology that were intractable just a few years ago.