Integrative modeling enables structure determination for large macromolecular assemblies by combining data from multiple experiments with theoretical and computational predictions. Recent advancements in AI-based structure prediction and cryo electron-microscopy have sparked renewed enthusiasm for integrative modeling; structures from AI-based methods can be integrated with in situ maps to characterize large assemblies. This approach previously allowed us and others to determine the architectures of diverse macromolecular assemblies, such as nuclear pore complexes, chromatin remodelers, and cell–cell junctions. Experimental data spanning several scales was used in these studies, ranging from high-resolution data, such as X-ray crystallography and AlphaFold structure, to low-resolution data, such as cryo-electron tomography maps and data from co-immunoprecipitation experiments. Two recurrent modeling challenges emerged across a range of studies. First, these assemblies contained significant fractions of disordered regions, necessitating the development of new methods for modeling disordered regions in the context of ordered regions. Second, methods needed to be developed to utilize the information from cryo-electron tomography, a timely challenge as structural biology is increasingly moving towards in situ characterization. Here, we recapitulate recent developments in the modeling of disordered proteins and the analysis of cryo-electron tomography data and highlight other opportunities for method development in the context of integrative modeling.

High resolution structures of protein complexes provide a wealth of information on protein structure and function. Databases of these protein structures are also used for artificial-intelligence (AI)-based methods of structural modelling. Despite the wealth of protein structures that have been determined by structural biologists, there are still gaps, or missing pieces in the puzzle of protein structural biology. Highly flexible regions may be missing from protein structures and conformational changes of different protein complex states may not be captured by current databases. In this perspective, I sketch out several ways that cross-linking mass spectrometry can contribute to filling in some of these missing pieces: Identification of cross-linked interactions in highly flexible protein regions not captured by other structural techniques; capturing conformational changes of protein complexes in different functional states; serving as distance constraints in integrative structural modelling and providing structural information of in cellulo proteins. The myriad ways in which cross-linking mass spectrometry contributes to filling in missing pieces in structural biology makes it a powerful technique in structural biology.

Protein circular dichroism (CD) and infrared absorbance (IR) spectra are widely used to estimate the secondary structure content of proteins in solution. A range of algorithms have been used for CD analysis (SELCON, CONTIN, CDsstr, SOMSpec) and some of these have been applied to IR data, though IR is more commonly analysed by bandfitting or statistical approaches. In this work we provide a Python version of SELCON3 and explore how to combine CD and IR data to best effect. We used CD data in Δε/amino acid residue and scaled the IR spectra to similar magnitudes. Normalising the IR amide I spectra scaled to a maximum absorbance of 15 gives best general performance. Combining CD and IR improves predictions for both helix and sheet by ~2% and helps identify anomalously large errors for high helix proteins such as haemoglobin when using IR data alone and high sheet proteins when using CD data alone.

In situ structural biology with cryo-electron tomography (cryo-ET) and subtomogram averaging (StA) is evolving as a major method to understand the structure, function, and interactions of biological molecules in cells in a single experiment. Since its inception, the method has matured with some stellar highlights and with further opportunities to broaden its applications. In this short review, I want to provide a personal perspective on the developments in cryo-ET as I have seen it for the last ~20 years and outline the major steps that led to its success. This perspective highlights cryo-ET with my eyes as a junior researcher and my view on the present and past developments in hardware and software for in situ structural biology with cryo-ET.

Host-virus interactions are critically important for various stages of the viral replication cycle. The reliance of viruses on the host factors for their entry, replication, and maturation processes can be exploited for the development of antiviral therapeutics. Thus, the identification and characterization of such viral-host dependency factors has been an attractive area of research to provide novel antiviral targets. Traditional proteomic efforts based on affinity purification of protein complexes from cell lysates are limited to detecting strong and stable interactions. In this perspective, we discuss the integration of two latest proteomic techniques, based on in situ proximity labelling and chemical crosslinking methods, to uncover host-virus protein–protein interactions in living cells.

Viruses are highly dynamic macromolecular assemblies. They undergo large-scale changes in structure and organization at nearly every stage of their infectious cycles from virion assembly to maturation, receptor docking, cell entry, uncoating and genome delivery. Understanding structural transformations and dynamics across the virus infectious cycle is an expansive area for research that that can also provide insight into mechanisms for blocking infection, replication, and transmission. Additionally, the processes viruses carry out serve as excellent model systems for analogous cellular processes, but in more accessible form. Capturing and analyzing these dynamic events poses a major challenge for many structural biological approaches due to the size and complexity of the assemblies and the heterogeneity and transience of the functional states that are populated. Here we examine the process of protein-mediated membrane fusion, which is carried out by specialized machinery on enveloped virus surfaces leading to delivery of the viral genome. Application of two complementary methods, cryo-electron tomography and structural mass spectrometry enable dynamic intermediate states in intact fusion systems to be imaged and probed, providing a new understanding of the mechanisms and machinery that drive this fundamental biological process.

Metabolic enzymes are the catalysts that drive the biochemical reactions essential for sustaining life. Many of these enzymes are tightly regulated by feedback mechanisms. To fully understand their roles and modulation, it is crucial to investigate the relationship between their structure, catalytic mechanism, and function. In this perspective, by using three examples from our studies on Mycobacterium tuberculosis (Mtb) isocitrate lyase and related proteins, we highlight how an integrated approach combining structural, activity, and biophysical data provides insights into their biological functions. These examples underscore the importance of employing fast-fail experiments at the early stages of a research project, emphasise the value of complementary techniques in validating findings, and demonstrate how in vitro data combined with chemical, biochemical, and physiological knowledge can lead to a broader understanding of metabolic adaptations in pathogenic bacteria. Finally, we address the unexplored questions in Mtb metabolism and discuss how we expand our approach to include microbiological and bioanalytical techniques to further our understanding. Such an integrated and interdisciplinary strategy has the potential to uncover novel regulatory mechanisms and identify new therapeutic opportunities for the eradication of tuberculosis. The approach can also be broadly applied to investigate other biochemical networks and complex biological systems.

Computational immunology has been the breeding ground of some of the best bioinformatics work of the day. By melding diverse data types, these approaches have been successful in associating genotypes with phenotypes. However, the representations (or spaces) in which these associations are mapped have primarily been constructed from some omics-oriented sequence data typically derived from high-throughput experiments. In this perspective, we highlight the importance of biophysical representations for performing the genotype–phenotype map. We contend that using biophysical representations reduces the dimensionality of a search problem, dramatically expedites the algorithm, and more importantly, offers physical interpretability to the classes of clustered sequences across different layers of complexity – molecular, cellular, or macro-level. Such biophysical interpretations offer a firm basis for the future of bioengineering and cell-based therapies.