Human mitochondrial protein complexes revealed by large-scale coevolution analysis and deep learning-based structure modeling

Mapping adipocyte interactome networks by HaloTag-enrichment-mass spectrometry

Mapping protein interaction complexes in their natural state in vivo is arguably the Holy Grail of protein network analysis. Detection of protein interaction stoichiometry has been an important technical challenge, as few studies have focused on this. This may, however, be solved by artificial intelligence (AI) and proteomics. Here, we describe the development of HaloTag-based affinity purification mass spectrometry (HaloMS), a high-throughput HaloMS assay for protein interaction discovery. The approach enables the rapid capture of newly expressed proteins, eliminating tedious conventional one-by-one assays. As a proof-of-principle, we used HaloMS to evaluate the protein complex interactions of 17 regulatory proteins in human adipocytes. The adipocyte interactome network was validated using an in vitro pull-down assay and AI-based prediction tools. Applying HaloMS to probe adipocyte differentiation facilitated the identification of previously unknown transcription factor (TF)-protein complexes, revealing proteome-wide human adipocyte TF networks and shedding light on how different pathways are integrated.

Integrative modeling meets deep learning: Recent advances in modeling protein assemblies

Recent progress in protein structure prediction based on deep learning revolutionized the field of Structural Biology. Beyond single proteins, it also enabled high-throughput prediction of structures of protein-protein interactions. Despite the success in predicting complex structures, large macromolecular assemblies still require specialized approaches. Here we describe recent advances in modeling macromolecular assemblies using integrative and hierarchical approaches. We highlight applications that predict protein-protein interactions and challenges in modeling complexes based on the interaction networks, including the prediction of complex stoichiometry and heterogeneity.

CHCHD4 binding affects the active site of apoptosis inducing factor (AIF): Structural determinants for allosteric regulation

Apoptosis-inducing factor (AIF), which is confined to mitochondria of normal healthy cells, is the first identified caspase-independent cell death effector. Moreover, AIF is required for the optimal functioning of the respiratory chain machinery. Recent findings have revealed that AIF fulfills its pro-survival function by interacting with CHCHD4, a soluble mitochondrial protein which promotes the entrance and the oxidative folding of different proteins in the inner membrane space. Here, we report the crystal structure of the ternary complex involving the N-terminal 27-mer peptide of CHCHD4, NAD+, and AIF harboring its FAD (flavin adenine dinucleotide) prosthetic group in oxidized form. Combining this information with biophysical and biochemical data on the CHCHD4/AIF complex, we provide a detailed structural description of the interaction between the two proteins, validated by both chemical cross-linking mass spectrometry analysis and site-directed mutagenesis.

Installation of LYRM proteins in early eukaryotes to regulate the metabolic capacity of the emerging mitochondrion

Core mitochondrial processes such as the electron transport chain, protein translation and the formation of Fe-S clusters (ISC) are of prokaryotic origin and were present in the bacterial ancestor of mitochondria. In animal and fungal models, a family of small Leu-Tyr-Arg motif-containing proteins (LYRMs) uniformly regulates the function of mitochondrial complexes involved in these processes. The action of LYRMs is contingent upon their binding to the acylated form of acyl carrier protein (ACP). This study demonstrates that LYRMs are structurally and evolutionarily related proteins characterized by a core triplet of α-helices. Their widespread distribution across eukaryotes suggests that 12 specialized LYRMs were likely present in the last eukaryotic common ancestor to regulate the assembly and folding of the subunits that are conserved in bacteria but that lack LYRM homologues. The secondary reduction of mitochondria to anoxic environments has rendered the function of LYRMs and their interaction with acylated ACP dispensable. Consequently, these findings strongly suggest that early eukaryotes installed LYRMs in aerobic mitochondria as orchestrated switches, essential for regulating core metabolism and ATP production.

Revolutionizing protein-protein interaction prediction with deep learning

Protein-protein interactions (PPIs) are pivotal for driving diverse biological processes, and any disturbance in these interactions can lead to disease. Thus, the study of PPIs has been a central focus in biology. Recent developments in deep learning methods, coupled with the vast genomic sequence data, have significantly boosted the accuracy of predicting protein structures and modeling protein complexes, approaching levels comparable to experimental techniques. Herein, we review the latest advances in the computational methods for modeling 3D protein complexes and the prediction of protein interaction partners, emphasizing the application of deep learning methods deriving from coevolution analysis. The review also highlights biomedical applications of PPI prediction and outlines challenges in the field.

A Perspective on the Prospective Use of AI in Protein Structure Prediction

AlphaFold2 (AF2) and RoseTTaFold (RF) have revolutionized structural biology, serving as highly reliable and effective methods for predicting protein structures. This article explores their impact and limitations, focusing on their integration into experimental pipelines and their application in diverse protein classes, including membrane proteins, intrinsically disordered proteins (IDPs), and oligomers. In experimental pipelines, AF2 models help X-ray crystallography in resolving the phase problem, while complementarity with mass spectrometry and NMR data enhances structure determination and protein flexibility prediction. Predicting the structure of membrane proteins remains challenging for both AF2 and RF due to difficulties in capturing conformational ensembles and interactions with the membrane. Improvements in incorporating membrane-specific features and predicting the structural effect of mutations are crucial. For intrinsically disordered proteins, AF2's confidence score (pLDDT) serves as a competitive disorder predictor, but integrative approaches including molecular dynamics (MD) simulations or hydrophobic cluster analyses are advocated for accurate dynamics representation. AF2 and RF show promising results for oligomeric models, outperforming traditional docking methods, with AlphaFold-Multimer showing improved performance. However, some caveats remain in particular for membrane proteins. Real-life examples demonstrate AF2's predictive capabilities in unknown protein structures, but models should be evaluated for their agreement with experimental data. Furthermore, AF2 models can be used complementarily with MD simulations. In this Perspective, we propose a "wish list" for improving deep-learning-based protein folding prediction models, including using experimental data as constraints and modifying models with binding partners or post-translational modifications. Additionally, a meta-tool for ranking and suggesting composite models is suggested, driving future advancements in this rapidly evolving field.

Exploring complexity of class-A Beta-lactamase family using physiochemical-based multiplex networks

The Beta-lactamase protein family is vital in countering Beta-lactam antibiotics, a widely used antimicrobial. To enhance our understanding of this family, we adopted a novel approach employing a multiplex network representation of its multiple sequence alignment. Each network layer, derived from the physiochemical properties of amino acids, unveils distinct insights into the intricate interactions among nodes, thereby enabling the identification of key motifs. Nodes with identical property signs tend to aggregate, providing evidence of the presence of consequential functional and evolutionary constraints shaping the Beta-lactamase family. We further investigate the distribution of evolutionary links across various layers. We observe that polarity manifests the highest number of unique links at lower thresholds, followed by hydrophobicity and polarizability, wherein hydrophobicity exerts dominance at higher thresholds. Further, the combinations of polarizability and volume, exhibit multiple simultaneous connections at all thresholds. The combination of hydrophobicity, polarizability, and volume uncovers shared links exclusive to these layers, implying substantial evolutionary impacts that may have functional or structural implications. By assessing the multi-degree of nodes, we unveil the hierarchical influence of properties at each position, identifying crucial properties responsible for the protein's functionality and providing valuable insights into potential targets for modulating enzymatic activity.

Computational analysis of regulatory regions in human protein kinases

Eukaryotic proteins often feature modular domain structures comprising globular domains that are connected by linker regions and intrinsically disordered regions that may contain important functional motifs. The intramolecular interactions of globular domains and nonglobular regions can play critical roles in different aspects of protein function. However, studying these interactions and their regulatory roles can be challenging due to the flexibility of nonglobular regions, the long insertions separating interacting modules, and the transient nature of some interactions. Obtaining the experimental structures of multiple domains and functional regions is more difficult than determining the structures of individual globular domains. High-quality structural models generated by AlphaFold offer a unique opportunity to study intramolecular interactions in eukaryotic proteins. In this study, we systematically explored intramolecular interactions between human protein kinase domains (KDs) and potential regulatory regions, including globular domains, N- and C-terminal tails, long insertions, and distal nonglobular regions. Our analysis identified intramolecular interactions between human KDs and 35 different types of globular domains, exhibiting a variety of interaction modes that could contribute to orthosteric or allosteric regulation of kinase activity. We also identified prevalent interactions between human KDs and their flanking regions (N- and C-terminal tails). These interactions exhibit group-specific characteristics and can vary within each specific kinase group. Although long-range interactions between KDs and nonglobular regions are relatively rare, structural details of these interactions offer new insights into the regulation mechanisms of several kinases, such as HASPIN, MAPK7, MAPK15, and SIK1B.

Structural Modeling of Cytokine-Receptor-JAK2 Signaling Complexes Using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). These cell-surface single-pass transmembrane (TM) glycoproteins regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains, and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all of the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated here by using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts, with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices, causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to the TM α-helices of the active TPOR dimer was proposed. The models also help elucidate the molecular basis of oncogenic mutations that may involve a noncanonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

Impact of Asp/Glu-ADP-ribosylation on protein-protein interaction and protein function

PARylation plays critical role in regulating multiple cellular processes such as DNA damage response and repair, transcription, RNA processing, and stress response. More than 300 human proteins have been found to be modified by PARylation on acidic residues, that is, Asp (D) and Glu (E). We used the deep-learning tool AlphaFold to predict protein-protein interactions (PPIs) and their interfaces for these proteins based on coevolution signals from joint multiple sequence alignments (MSAs). AlphaFold predicted 260 confident PPIs involving PARylated proteins, and about one quarter of these PPIs have D/E-PARylation sites in their predicted PPI interfaces. AlphaFold predictions offer novel insights into the mechanisms of PARylation regulations by providing structural details of the PPI interfaces. D/E-PARylation sites have a preference to occur in coil regions and disordered regions, and PPI interfaces containing D/E-PARylation sites tend to occur between short linear sequence motifs in disordered regions and globular domains. The hub protein PCNA is predicted to interact with more than 20 proteins via the common PIP box motif and the structurally variable flanking regions. D/E-PARylation sites were found in the interfaces of key components of the RNA transcription and export complex, the SF3a spliceosome complex, and H/ACA and C/D small nucleolar ribonucleoprotein complexes, suggesting that systematic PARylation have a profound effect in regulating multiple RNA-related processes such as RNA nuclear export, splicing, and modification. Finally, PARylation of SUMO2 could modulate its interaction with CHAF1A, thereby representing a potential mechanism for the cross-talk between PARylation and SUMOylation in regulation of chromatin remodeling.

A two-step mitochondrial import pathway couples the disulfide relay with matrix complex I biogenesis

Mitochondria critically rely on protein import and its tight regulation. Here, we found that the complex I assembly factor NDUFAF8 follows a two-step import pathway linking IMS and matrix import systems. A weak targeting sequence drives TIM23-dependent NDUFAF8 matrix import, and en route, allows exposure to the IMS disulfide relay, which oxidizes NDUFAF8. Import is closely surveyed by proteases: YME1L prevents accumulation of excess NDUFAF8 in the IMS, while CLPP degrades reduced NDUFAF8 in the matrix. Therefore, NDUFAF8 can only fulfil its function in complex I biogenesis if both oxidation in the IMS and subsequent matrix import work efficiently. We propose that the two-step import pathway for NDUFAF8 allows integration of the activity of matrix complex I biogenesis pathways with the activity of the mitochondrial disulfide relay system in the IMS. Such coordination might not be limited to NDUFAF8 as we identified further proteins that can follow such a two-step import pathway.

Recent Advances in Deep Learning for Protein-Protein Interaction Analysis: A Comprehensive Review

Deep learning, a potent branch of artificial intelligence, is steadily leaving its transformative imprint across multiple disciplines. Within computational biology, it is expediting progress in the understanding of Protein-Protein Interactions (PPIs), key components governing a wide array of biological functionalities. Hence, an in-depth exploration of PPIs is crucial for decoding the intricate biological system dynamics and unveiling potential avenues for therapeutic interventions. As the deployment of deep learning techniques in PPI analysis proliferates at an accelerated pace, there exists an immediate demand for an exhaustive review that encapsulates and critically assesses these novel developments. Addressing this requirement, this review offers a detailed analysis of the literature from 2021 to 2023, highlighting the cutting-edge deep learning methodologies harnessed for PPI analysis. Thus, this review stands as a crucial reference for researchers in the discipline, presenting an overview of the recent studies in the field. This consolidation helps elucidate the dynamic paradigm of PPI analysis, the evolution of deep learning techniques, and their interdependent dynamics. This scrutiny is expected to serve as a vital aid for researchers, both well-established and newcomers, assisting them in maneuvering the rapidly shifting terrain of deep learning applications in PPI analysis.

Structural modeling of cytokine-receptor-JAK2 signaling complexes using AlphaFold Multimer

Homodimeric class 1 cytokine receptors include the erythropoietin (EPOR), thrombopoietin (TPOR), granulocyte colony-stimulating factor 3 (CSF3R), growth hormone (GHR), and prolactin receptors (PRLR). They are cell-surface single-pass transmembrane (TM) glycoproteins that regulate cell growth, proliferation, and differentiation and induce oncogenesis. An active TM signaling complex consists of a receptor homodimer, one or two ligands bound to the receptor extracellular domains and two molecules of Janus Kinase 2 (JAK2) constitutively associated with the receptor intracellular domains. Although crystal structures of soluble extracellular domains with ligands have been obtained for all the receptors except TPOR, little is known about the structure and dynamics of the complete TM complexes that activate the downstream JAK-STAT signaling pathway. Three-dimensional models of five human receptor complexes with cytokines and JAK2 were generated using AlphaFold Multimer. Given the large size of the complexes (from 3220 to 4074 residues), the modeling required a stepwise assembly from smaller parts with selection and validation of the models through comparisons with published experimental data. The modeling of active and inactive complexes supports a general activation mechanism that involves ligand binding to a monomeric receptor followed by receptor dimerization and rotational movement of the receptor TM α-helices causing proximity, dimerization, and activation of associated JAK2 subunits. The binding mode of two eltrombopag molecules to TM α-helices of the active TPOR dimer was proposed. The models also help elucidating the molecular basis of oncogenic mutations that may involve non-canonical activation route. Models equilibrated in explicit lipids of the plasma membrane are publicly available.

Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes

Advances in a scientific discipline are often measured by small, incremental steps. In this review, we report on two intertwined disciplines in the protein structure prediction field, modeling of single chains and modeling of complexes, that have over decades emulated this pattern, as monitored by the community-wide blind prediction experiments CASP and CAPRI. However, over the past few years, dramatic advances were observed for the accurate prediction of single protein chains, driven by a surge of deep learning methodologies entering the prediction field. We review the mainscientific developments that enabled these recent breakthroughs and feature the important role of blind prediction experiments in building up and nurturing the structure prediction field. We discuss how the new wave of artificial intelligence-based methods is impacting the fields of computational and experimental structural biology and highlight areas in which deep learning methods are likely to lead to future developments, provided that major challenges are overcome.

Recent advances in predicting and modeling protein-protein interactions

Protein-protein interactions (PPIs) drive biological processes, and disruption of PPIs can cause disease. With recent breakthroughs in structure prediction and a deluge of genomic sequence data, computational methods to predict PPIs and model spatial structures of protein complexes are now approaching the accuracy of experimental approaches for permanent interactions and show promise for elucidating transient interactions. As we describe here, the key to this success is rich evolutionary information deciphered from thousands of homologous sequences that coevolve in interacting partners. This covariation signal, revealed by sophisticated statistical and machine learning (ML) algorithms, predicts physiological interactions. Accurate artificial intelligence (AI)-based modeling of protein structures promises to provide accurate 3D models of PPIs at a proteome-wide scale.

AIFM1 beyond cell death: An overview of this OXPHOS-inducing factor in mitochondrial diseases

Apoptosis-inducing factor (AIF) is a mitochondrial intermembrane space flavoprotein with diverse functions in cellular physiology. In this regard, a large number of studies have elucidated AIF's participation to chromatin condensation during cell death in development, cancer, cardiovascular and brain disorders. However, the discovery of rare AIFM1 mutations in patients has shifted the interest of biomedical researchers towards AIF's contribution to pathogenic mechanisms underlying inherited AIFM1-linked metabolic diseases. The functional characterization of AIF binding partners has rapidly advanced our understanding of AIF biology within the mitochondria and beyond its widely reported role in cell death. At the present time, it is reasonable to assume that AIF contributes to cell survival by promoting biogenesis and maintenance of the mitochondrial oxidative phosphorylation (OXPHOS) system. With this review, we aim to outline the current knowledge around the vital role of AIF by primarily focusing on currently reported human diseases that have been linked to AIFM1 deficiency.