Computational Structural Biology: Successes, Future Directions, and Challenges

Integrative modeling of diverse protein-peptide systems using CABS-dock

The CABS model can be applied to a wide range of protein-protein and protein-peptide molecular modeling tasks, such as simulating folding pathways, predicting structures, docking, and analyzing the structural dynamics of molecular complexes. In this work, we use the CABS-dock tool in two diverse modeling tasks: 1) predicting the structures of amyloid protofilaments and 2) identifying cleavage sites in the peptide substrates of proteolytic enzymes. In the first case, simulations of the simultaneous docking of amyloidogenic peptides indicated that the CABS model can accurately predict the structures of amyloid protofilaments which have an in-register parallel architecture. Scoring based on a combination of symmetry criteria and estimated interaction energy values for bound monomers enables the identification of protofilament models that closely match their experimental structures for 5 out of 6 analyzed systems. For the second task, it has been shown that CABS-dock coarse-grained docking simulations can be used to identify the positions of cleavage sites in the peptide substrates of proteolytic enzymes. The cleavage site position was correctly identified for 12 out of 15 analyzed peptides. When combined with sequence-based methods, these docking simulations may lead to an efficient way of predicting cleavage sites in degraded proteins. The method also provides the atomic structures of enzyme-substrate complexes, which can give insights into enzyme-substrate interactions that are crucial for the design of new potent inhibitors.

Integrating computational skills in undergraduate Microbiology curricula in developing countries

The employability of young graduates has gained increasing significance in the labour market of the 21st century. Universities turn out millions of graduates annually, but at the same time, employers highlight their lack of the requisite skills for sustainable employment. We live today in a world of data, and therefore courses that feature numerical and computational tools to gather and analyse data are to be sourced for and integrated into life sciences' curricula as they provide a number of benefits for both the students and faculty members that are engaged in teaching the courses. The lack of this teaching in undergraduate Microbiology curricula is devastating and leaves a knowledge gap in the graduates that are turned out. This results in an inability of the emerging graduates to compete favourably with their counterparts from other parts of the world. There is a necessity on the part of life science educators to adapt their teaching strategies to best support students' curricula that prepare them for careers in science. Bioinformatics, Statistics and Programming are key computational skills to embrace by life scientists and the need for training beginning at undergraduate level cannot be overemphasized. This article reviews the need to integrate computational skills in undergraduate Microbiology curricula in developing countries with emphasis on Nigeria.

Pandemic strategies with computational and structural biology against COVID-19: A retrospective

The emergence of the Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which is the etiologic agent of the coronavirus disease 2019 (COVID-19) pandemic, has dominated all aspects of life since of 2020. Research studies on the virus and exploration of therapeutic and preventive strategies has been moving at rapid rates to control the pandemic. In the field of bioinformatics or computational and structural biology, recent research strategies have used multiple disciplines to compile large datasets to uncover statistical correlations and significance, visualize and model proteins, perform molecular dynamics simulations, and employ the help of artificial intelligence and machine learning to harness computational processing power to further the research on COVID-19, including drug screening, drug design, vaccine development, prognosis prediction, and outbreak prediction. These recent developments should help us better understand the viral disease and develop the much-needed therapies and strategies for the management of COVID-19.

Generating tertiary protein structures via interpretable graph variational autoencoders

Motivation

Modeling the structural plasticity of protein molecules remains challenging. Most research has focused on obtaining one biologically active structure. This includes the recent AlphaFold2 that has been hailed as a breakthrough for protein modeling. Computing one structure does not suffice to understand how proteins modulate their interactions and even evade our immune system. Revealing the structure space available to a protein remains challenging. Data-driven approaches that learn to generate tertiary structures are increasingly garnering attention. These approaches exploit the ability to represent tertiary structures as contact or distance maps and make direct analogies with images to harness convolution-based generative adversarial frameworks from computer vision. Since such opportunistic analogies do not allow capturing highly structured data, current deep models struggle to generate physically realistic tertiary structures.

Results

We present novel deep generative models that build upon the graph variational autoencoder framework. In contrast to existing literature, we represent tertiary structures as 'contact' graphs, which allow us to leverage graph-generative deep learning. Our models are able to capture rich, local and distal constraints and additionally compute disentangled latent representations that reveal the impact of individual latent factors. This elucidates what the factors control and makes our models more interpretable. Rigorous comparative evaluation along various metrics shows that the models, we propose advance the state-of-the-art. While there is still much ground to cover, the work presented here is an important first step, and graph-generative frameworks promise to get us to our goal of unraveling the exquisite structural complexity of protein molecules.

Availability and implementation

Code is available at https://github.com/anonymous1025/CO-VAE.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Undergraduate structural biology education: A shift from users to developers of computation and simulation tools

The use of theory and simulation in undergraduate education in biochemistry, molecular biology, and structural biology is now common, but the skills students need and the curriculum instructors have to train their students are evolving. The global pandemic and the immediate switch to remote instruction forced instructors to reconsider how they can use computation to teach concepts previously approached with other instructional methods. In this review, we survey some of the curricula, materials, and resources for instructors who want to include theory, simulation, and computation in the undergraduate curriculum. There has been a notable progression from teaching students to use discipline-specific computational tools to developing interactive computational tools that promote active learning to having students write code themselves, such that they view computation as another tool for solving problems. We are moving toward a future where computational skills, including programming, data analysis, visualization, and simulation, will no longer be considered an optional bonus for students but a required skill for the 21st century STEM (Science, Technology, Engineering, and Mathematics) workforce; therefore, all physical and life science students should learn to program in the undergraduate curriculum.

Generative Adversarial Learning of Protein Tertiary Structures

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.

Bioinformatic analysis of subfamily-specific regions in 3D-structures of homologs to study functional diversity and conformational plasticity in protein superfamilies

Local 3D-structural differences in homologous proteins contribute to functional diversity observed in a superfamily, but so far received little attention as bioinformatic analysis was usually carried out at the level of amino acid sequences. We have developed Zebra3D - the first-of-its-kind bioinformatic software for systematic analysis of 3D-alignments of protein families using machine learning. The new tool identifies subfamily-specific regions (SSRs) - patterns of local 3D-structure (i.e. single residues, loops, or secondary structure fragments) that are spatially equivalent within families/subfamilies, but are different among them, and thus can be associated with functional diversity and function-related conformational plasticity. Bioinformatic analysis of protein superfamilies by Zebra3D can be used to study 3D-determinants of catalytic activity and specific accommodation of ligands, help to prepare focused libraries for directed evolution or assist development of chimeric enzymes with novel properties by exchange of equivalent regions between homologs, and to characterize plasticity in binding sites. A companion Mustguseal web-server is available to automatically construct a 3D-alignment of functionally diverse proteins, thus reducing the minimal input required to operate Zebra3D to a single PDB code. The Zebra3D + Mustguseal combined approach provides the opportunity to systematically explore the value of SSRs in superfamilies and to use this information for protein design and drug discovery. The software is available open-access at https://biokinet.belozersky.msu.ru/Zebra3D.

The Challenges of 21st Century Neurotoxicology: The Case of Neurotoxicology Applied to Nanomaterials

After a short background discussing engineered nanomaterials (ENMs) and their physicochemical properties and applications, the present perspective paper highlights the main specific points that need to be considered when examining the question of neurotoxicity of nanomaterials. It underlines the necessity to integrate parameters, specific tools, and tests from multiple sources that make neurotoxicology when applied to nanomaterials particularly complex. Bringing together the knowledge of multiple disciplines e.g., nanotoxicology to neurotoxicology, is necessary to build integrated neurotoxicology for the third decade of the 21st Century. This article focuses on the greatest challenges and opportunities offered by this specific field. It highlights the scientific, methodological, political, regulatory, and educational issues. Scientific and methodological challenges include the determination of ENMs physicochemical parameters, the lack of information about protein corona modes of action, target organs, and cells and dose– response functions of ENMs. The need of standardization of data collection and harmonization of dedicated neurotoxicological protocols are also addressed. This article highlights how to address those challenges through innovative methods and tools, and our work also ventures to sketch the first list of substances that should be urgently prioritized for human modern neurotoxicology. Finally, political support with dedicated funding at the national and international levels must also be used to engage the communities concerned to set up dedicated educational program on this novel field.

Unsupervised multi-instance learning for protein structure determination

Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity remains popular. However, it is unclear how exactly to exploit the groups of decoys revealed via clustering to select individual structures for prediction. In this paper, we provide an intuitive formulation of the decoy selection problem as an instance of unsupervised multi-instance learning. We address the problem in three stages, first organizing given decoys of a protein molecule into bags, then identifying relevant bags, and finally drawing individual instances from these bags to offer as prediction. We propose both non-parametric and parametric algorithms for drawing individual instances. Our evaluation utilizes two datasets, one benchmark dataset of ensembles of decoys for a varied list of protein molecules, and a dataset of decoy ensembles for targets drawn from recent CASP competitions. A comparative analysis with state-of-the-art methods reveals that the proposed approach outperforms existing methods, thus warranting further investigation of multi-instance learning to advance our treatment of decoy selection.

Catalytic and lectin domains in neuraminidase A from Streptococcus pneumoniae are capable of an intermolecular assembly: Implications for biofilm formation

Neuraminidase A from Streptococcus pneumoniae (NanA) is a cell wall-bound modular enzyme containing one lectin and one catalytic domain. Unlike homologous NanB and NanC expressed by the same bacterium, the two domains within one NanA molecule do not form a stable interaction and are spatially separated by a 16-amino acid-long flexible linker. In this work, the ability of NanA to form intermolecular assemblies was characterized using the methods of molecular modeling and bioinformatic analysis based on crystallographic data and by bringing together previously published experimental data. It was concluded that two catalytic domains, as well as one catalytic and one lectin domain, originating from two cell wall-bound NanA molecules, can interact through a previously uncharacterized interdomain interface to form complexes stabilized by a network of intermolecular hydrogen bonds and salt bridges. Supercomputer modeling strongly indicated that artocarpin, an earlier experimentally discovered inhibitor of the pneumococcal biofilm formation, is able to bind to a site located in the catalytic domain of one NanA entity and prevent its interaction with the lectin or catalytic domain of another NanA entity, thus directly precluding the generation of intermolecular assemblies. The revealed structural adaptation is discussed as one plausible mechanism of noncatalytic participation of this potentially key pathogenicity enzyme in pneumococcal biofilm formation.

High-throughput quantitative top-down proteomics

Top-down mass spectrometry (MS) analyzes intact proteins at the proteoform level, which allows researchers to better understand the functions of protein modifications. Recently, top-down proteomics has increased in popularity due to advancements in high-resolution mass spectrometers, increased efficiency in liquid chromatography (LC) separation, and advances in data analysis software. Some unique protein proteoforms, which have been distinguished using top-down MS, have even been shown to exhibit marked variation in biological function compared to similar proteoforms. However, the qualitative identification of a particular proteoform may not be enough to determine the biological relevance of that proteoform. Quantitative top-down MS methods have been notably applied to the study of the differing biological functions of protein proteoforms and have allowed researchers to explore proteomes at the proteoform, rather than the peptide, level. Here, we review the top-down MS methods that have been used to quantitatively identify intact proteins, discuss current applications of quantitative top-down MS analysis, and present new areas where quantitative top-down MS analysis may be implemented.

Uncovering the structure and function of Pseudomonas aeruginosa periplasmic proteins by an in silico approach

Pseudomonas aeruginosa is an opportunistic human pathogen highly relevant from a biomedical viewpoint. It is one of the main causes of infection in hospitalized patients and a major cause of mortality of cystic fibrosis patients. This is also due to its ability to develop resistance to antibiotics by various mechanisms. Therefore, it is urgent and desirable to identify novel targets for the development of new antibacterial drugs against Pseudomonas aeruginosa. In this work this problem was tackled by an in silico approach aimed at providing a reliable structural model and functional annotation for the Pseudomonas aeruginosa periplasmic proteins for which these data are not available yet. A total of 83 protein sequences were analyzed, and the corresponding structural models were built, leading to the identification of 32 periplasmic 'substrate-binding proteins', 14 enzymes and 4 proteins with different functions, including lipids and metals binding. The most interesting cases were found within the 'enzymes' group with the identification of a lipase, which can be regarded as a virulence factor, a protease involved in the assembly of β-barrel membrane proteins and a l,d-transpeptidase, which could contribute to confer resistance to β-lactam antibiotics to the bacterium.Communicated by Ramaswamy H. Sarma.