Benchmarking AlphaFold for protein complex modeling reveals accuracy determinants

High‐resolution experimental structural determination of protein–protein interactions has led to valuable mechanistic insights, yet due to the massive number of interactions and experimental limitations there is a need for computational methods that can accurately model their structures. Here we explore the use of the recently developed deep learning method, AlphaFold, to predict structures of protein complexes from sequence. With a benchmark of 152 diverse heterodimeric protein complexes, multiple implementations and parameters of AlphaFold were tested for accuracy. Remarkably, many cases (43%) had near‐native models (medium or high critical assessment of predicted interactions accuracy) generated as top‐ranked predictions by AlphaFold, greatly surpassing the performance of unbound protein–protein docking (9% success rate for near‐native top‐ranked models), however AlphaFold modeling of antibody–antigen complexes within our set was unsuccessful. We identified sequence and structural features associated with lack of AlphaFold success, and we also investigated the impact of multiple sequence alignment input. Benchmarking of a multimer‐optimized version of AlphaFold (AlphaFold‐Multimer) with a set of recently released antibody–antigen structures confirmed a low rate of success for antibody–antigen complexes (11% success), and we found that T cell receptor–antigen complexes are likewise not accurately modeled by that algorithm, showing that adaptive immune recognition poses a challenge for the current AlphaFold algorithm and model. Overall, our study demonstrates that end‐to‐end deep learning can accurately model many transient protein complexes, and highlights areas of improvement for future developments to reliably model any protein–protein interaction of interest.     

Complementing machine learning‐based structure predictions with native mass spectrometry

The advent of machine learning‐based structure prediction algorithms such as AlphaFold2 (AF2) and RoseTTa Fold have moved the generation of accurate structural models for the entire cellular protein machinery into the reach of the scientific community. However, structure predictions of protein complexes are based on user‐provided input and may require experimental validation. Mass spectrometry (MS) is a versatile, time‐effective tool that provides information on post‐translational modifications, ligand interactions, conformational changes, and higher‐order oligomerization. Using three protein systems, we show that native MS experiments can uncover structural features of ligand interactions, homology models, and point mutations that are undetectable by AF2 alone. We conclude that machine learning can be complemented with MS to yield more accurate structural models on a small and large scale.     

Membranome 3.0: Database of single‐pass membrane proteins with AlphaFold models

The Membranome database provides comprehensive structural information on single‐pass (i.e., bitopic) membrane proteins from six evolutionarily distant organisms, including protein–protein interactions, complexes, mutations, experimental structures, and models of transmembrane α‐helical dimers. We present a new version of this database, Membranome 3.0, which was significantly updated by revising the set of 5,758 bitopic proteins and incorporating models generated by AlphaFold 2 in the database. The AlphaFold models were parsed into structural domains located at the different membrane sides, modified to exclude low‐confidence unstructured terminal regions and signal sequences, validated through comparison with available experimental structures, and positioned with respect to membrane boundaries. Membranome 3.0 was re‐developed to facilitate visualization and comparative analysis of multiple 3D structures of proteins that belong to a specified family, complex, biological pathway, or membrane type. New tools for advanced search and analysis of proteins, their interactions, complexes, and mutations were included. The database is freely accessible at https://membranome.org.     

Mass spectrometry‐based protein–protein interaction networks for the study of human diseases

A better understanding of the molecular mechanisms underlying disease is key for expediting the development of novel therapeutic interventions. Disease mechanisms are often mediated by interactions between proteins. Insights into the physical rewiring of protein–protein interactions in response to mutations, pathological conditions, or pathogen infection can advance our understanding of disease etiology, progression, and pathogenesis and can lead to the identification of potential druggable targets. Advances in quantitative mass spectrometry (MS)‐based approaches have allowed unbiased mapping of these disease‐mediated changes in protein–protein interactions on a global scale. Here, we review MS techniques that have been instrumental for the identification of protein–protein interactions at a system‐level, and we discuss the challenges associated with these methodologies as well as novel MS advancements that aim to address these challenges. An overview of examples from diverse disease contexts illustrates the potential of MS‐based protein–protein interaction mapping approaches for revealing disease mechanisms, pinpointing new therapeutic targets, and eventually moving toward personalized applications.     

Network pharmacology and molecular docking analyses on Lianhua Qingwen capsule indicate Akt1 is a potential target to treat and prevent COVID‐19

ObjectivesCoronavirus disease 2019 (COVID‐19) is rapidly spreading worldwide. Lianhua Qingwen capsule (LQC) has shown therapeutic effects in patients with COVID‐19. This study is aimed to discover its molecular mechanism and provide potential drug targets.Materials and MethodsAn LQC target and COVID‐19–related gene set was established using the Traditional Chinese Medicine Systems Pharmacology database and seven disease‐gene databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis and protein‐protein interaction (PPI) network were performed to discover the potential mechanism. Molecular docking was performed to visualize the patterns of interactions between the effective molecule and targeted protein.ResultsA gene set of 65 genes was generated. We then constructed a compound‐target network that contained 234 nodes of active compounds and 916 edges of compound‐target pairs. The GO and KEGG indicated that LQC can act by regulating immune response, apoptosis and virus infection. PPI network and subnetworks identified nine hub genes. The molecular docking was conducted on the most significant gene Akt1, which is involved in lung injury, lung fibrogenesis and virus infection. Six active compounds of LQC can enter the active pocket of Akt1, namely beta‐carotene, kaempferol, luteolin, naringenin, quercetin and wogonin, thereby exerting potential therapeutic effects in COVID‐19.ConclusionsThe network pharmacological strategy integrates molecular docking to unravel the molecular mechanism of LQC. Akt1 is a promising drug target to reduce tissue damage and help eliminate virus infection.     

Interactomes of SARS‐CoV‐2 and human coronaviruses reveal host factors potentially affecting pathogenesis

Host–virus protein–protein interactions play key roles in the life cycle of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). We conducted a comprehensive interactome study between the virus and host cells using tandem affinity purification and proximity‐labeling strategies and identified 437 human proteins as the high‐confidence interacting proteins. Further characterization of these interactions and comparison to other large‐scale study of cellular responses to SARS‐CoV‐2 infection elucidated how distinct SARS‐CoV‐2 viral proteins participate in its life cycle. With these data mining, we discovered potential drug targets for the treatment of COVID‐19. The interactomes of two key SARS‐CoV‐2‐encoded viral proteins, NSP1 and N, were compared with the interactomes of their counterparts in other human coronaviruses. These comparisons not only revealed common host pathways these viruses manipulate for their survival, but also showed divergent protein–protein interactions that may explain differences in disease pathology. This comprehensive interactome of SARS‐CoV‐2 provides valuable resources for the understanding and treating of this disease.     

SARS‐CoV‐2–host proteome interactions for antiviral drug discovery

Treatment options for COVID‐19, caused by SARS‐CoV‐2, remain limited. Understanding viral pathogenesis at the molecular level is critical to develop effective therapy. Some recent studies have explored SARS‐CoV‐2–host interactomes and provided great resources for understanding viral replication. However, host proteins that functionally associate with SARS‐CoV‐2 are localized in the corresponding subnetwork within the comprehensive human interactome. Therefore, constructing a downstream network including all potential viral receptors, host cell proteases, and cofactors is necessary and should be used as an additional criterion for the validation of critical host machineries used for viral processing. This study applied both affinity purification mass spectrometry (AP‐MS) and the complementary proximity‐based labeling MS method (BioID‐MS) on 29 viral ORFs and 18 host proteins with potential roles in viral replication to map the interactions relevant to viral processing. The analysis yields a list of 693 hub proteins sharing interactions with both viral baits and host baits and revealed their biological significance for SARS‐CoV‐2. Those hub proteins then served as a rational resource for drug repurposing via a virtual screening approach. The overall process resulted in the suggested repurposing of 59 compounds for 15 protein targets. Furthermore, antiviral effects of some candidate drugs were observed in vitro validation using image‐based drug screen with infectious SARS‐CoV‐2. In addition, our results suggest that the antiviral activity of methotrexate could be associated with its inhibitory effect on specific protein–protein interactions.     

Systematic discovery of linear binding motifs targeting an ancient protein interaction surface on MAP kinases

Mitogen‐activated protein kinases (MAPK) are broadly used regulators of cellular signaling. However, how these enzymes can be involved in such a broad spectrum of physiological functions is not understood. Systematic discovery of MAPK networks both experimentally and in silico has been hindered because MAPKs bind to other proteins with low affinity and mostly in less‐characterized disordered regions. We used a structurally consistent model on kinase‐docking motif interactions to facilitate the discovery of short functional sites in the structurally flexible and functionally under‐explored part of the human proteome and applied experimental tools specifically tailored to detect low‐affinity protein–protein interactions for their validation in vitro and in cell‐based assays. The combined computational and experimental approach enabled the identification of many novel MAPK‐docking motifs that were elusive for other large‐scale protein–protein interaction screens. The analysis produced an extensive list of independently evolved linear binding motifs from a functionally diverse set of proteins. These all target, with characteristic binding specificity, an ancient protein interaction surface on evolutionarily related but physiologically clearly distinct three MAPKs (JNK, ERK, and p38). This inventory of human protein kinase binding sites was compared with that of other organisms to examine how kinase‐mediated partnerships evolved over time. The analysis suggests that most human MAPK‐binding motifs are surprisingly new evolutionarily inventions and newly found links highlight (previously hidden) roles of MAPKs. We propose that short MAPK‐binding stretches are created in disordered protein segments through a variety of ways and they represent a major resource for ancient signaling enzymes to acquire new regulatory roles.     

Analysis of insertions and extensions in the functional evolution of the ribonucleotide reductase family

Ribonucleotide reductases (RNRs) are used by all free‐living organisms and many viruses to catalyze an essential step in the de novo biosynthesis of DNA precursors. RNRs are remarkably diverse by primary sequence and cofactor requirement, while sharing a conserved fold and radical‐based mechanism for nucleotide reduction. In this work, we expand on our recent phylogenetic inference of the entire RNR family and describe the evolutionarily relatedness of insertions and extensions around the structurally homologous catalytic barrel. Using evo‐velocity and sequence similarity network (SSN) analyses, we show that the N‐terminal regulatory motif known as the ATP‐cone domain was likely inherited from an ancestral RNR. By combining SSN analysis with AlphaFold2 predictions, we also show that the C‐terminal extensions of class II RNRs can contain folded domains that share homology with an Fe‐S cluster assembly protein. Finally, using sequence analysis and AlphaFold2, we show that the sequence motif of a catalytically essential insertion known as the finger loop is tightly coupled to the catalytic mechanism. Based on these results, we propose an evolutionary model for the diversification of the RNR family.     

ProPose: a docking engine based on a fully configurable protein–ligand interaction model

Virtual high-throughput screening of molecular databases and in particular high-throughput protein–ligand docking are both common methodologies that identify and enrich hits in the early stages of the drug design process. Current protein–ligand docking algorithms often implement a program-specific model for protein–ligand interaction geometries. However, in order to create a platform for arbitrary queries in molecular databases, a new program is desirable that allows more manual control of the modeling of molecular interactions.For that reason, ProPose, an advanced incremental construction docking engine, is presented here that implements a fast and fully configurable molecular interaction and scoring model. This program uses user-defined, discrete, pharmacophore-like representations of molecular interactions that are transformed on-the-fly into a continuous potential energy surface, allowing for the incorporation of target specific interaction mechanisms into docking protocols in a straightforward manner. A torsion angle library, based on semi-empirical quantum chemistry calculations, is used to provide minimum energy torsion angles for the incremental construction algorithm. Docking results of a diverse set of protein–ligand complexes from the Protein Data Bank demonstrate the feasibility of this new approach.As a result, the seamless integration of pharmacophore-like interaction types into the docking and scoring scheme implemented in ProPose opens new opportunities for efficient, receptor-specific screening protocols. Figure ProPose — a fully configurable protein-ligand docking program — transforms pharmacophores into a smooth potential energy surface.This revised version was published online in October 2004 with corrections to the Graphical Abstract.     

Learning deep representations of enzyme thermal adaptation

Temperature is a fundamental environmental factor that shapes the evolution of organisms. Learning thermal determinants of protein sequences in evolution thus has profound significance for basic biology, drug discovery, and protein engineering. Here, we use a data set of over 3 million BRENDA enzymes labeled with optimal growth temperatures (OGTs) of their source organisms to train a deep neural network model (DeepET). The protein‐temperature representations learned by DeepET provide a temperature‐related statistical summary of protein sequences and capture structural properties that affect thermal stability. For prediction of enzyme optimal catalytic temperatures and protein melting temperatures via a transfer learning approach, our DeepET model outperforms classical regression models trained on rationally designed features and other deep‐learning‐based representations. DeepET thus holds promise for understanding enzyme thermal adaptation and guiding the engineering of thermostable enzymes.     

PaFlexPepDock: Parallel Ab-Initio Docking of Peptides onto Their Receptors with Full Flexibility Based on Rosetta

Structural information related to protein–peptide complexes can be very useful for novel drug discovery and design. The computational docking of protein and peptide can supplement the structural information available on protein–peptide interactions explored by experimental ways. Protein–peptide docking of this paper can be described as three processes that occur in parallel: ab-initio peptide folding, peptide docking with its receptor, and refinement of some flexible areas of the receptor as the peptide is approaching. Several existing methods have been used to sample the degrees of freedom in the three processes, which are usually triggered in an organized sequential scheme. In this paper, we proposed a parallel approach that combines all the three processes during the docking of a folding peptide with a flexible receptor. This approach mimics the actual protein–peptide docking process in parallel way, and is expected to deliver better performance than sequential approaches. We used 22 unbound protein–peptide docking examples to evaluate our method. Our analysis of the results showed that the explicit refinement of the flexible areas of the receptor facilitated more accurate modeling of the interfaces of the complexes, while combining all of the moves in parallel helped the constructing of energy funnels for predictions.     

Ligand-Target Prediction by Structural Network Biology Using nAnnoLyze

Target identification is essential for drug design, drug-drug interaction prediction, dosage adjustment and side effect anticipation. Specifically, the knowledge of structural details is essential for understanding the mode of action of a compound on a target protein. Here, we present nAnnoLyze, a method for target identification that relies on the hypothesis that structurally similar binding sites bind similar ligands. nAnnoLyze integrates structural information into a bipartite network of interactions and similarities to predict structurally detailed compound-protein interactions at proteome scale. The method was benchmarked on a dataset of 6,282 pairs of known interacting ligand-target pairs reaching a 0.96 of area under the Receiver Operating Characteristic curve (AUC) when using the drug names as an input feature for the classifier, and a 0.70 of AUC for “anonymous” compounds or compounds not present in the training set. nAnnoLyze resulted in higher accuracies than its predecessor, AnnoLyze. We applied the method to predict interactions for all the compounds in the DrugBank database with each human protein structure and provide examples of target identification for known drugs against human diseases. The accuracy and applicability of our method to any compound indicate that a comparative docking approach such as nAnnoLyze enables large-scale annotation and analysis of compound–protein interactions and thus may benefit drug development.     

Tracking exogenous intracellular casp‐3 using split GFP

Cytosolic protein delivery promises diverse applications from therapeutics, to genetic modification and precision research tools. To achieve effective cellular and subcellular delivery, approaches that allow protein visualization and accurate localization with greater sensitivity are essential. Fluorescently tagging proteins allows detection, tracking and visualization in cellulo. However, undesired consequences from fluorophores or fluorescent protein tags, such as nonspecific interactions and high background or perturbation to native protein''s size and structure, are frequently observed, or more troublingly, overlooked. Distinguishing cytosolically released molecules from those that are endosomally entrapped upon cellular uptake is particularly challenging and is often complicated by the inherent pH‐sensitive and hydrophobic properties of the fluorophore. Monitoring localization is more complex in delivery of proteins with inherent protein‐modifying activities like proteases, transacetylases, kinases, etc. Proteases are among the toughest cargos due to their inherent propensity for self‐proteolysis. To implement a reliable, but functionally silent, tagging technology in a protease, we have developed a caspase‐3 variant tagged with the 11th strand of GFP that retains both enzymatic activity and structural characteristics of wild‐type caspase‐3. Only in the presence of cytosolic GFP strands 1–10 will the tagged caspase‐3 generate fluorescence to signal a non‐endosomal location. This methodology facilitates easy screening of cytosolic vs. endosomally‐entrapped proteins due to low probabilities for false positive results, and further, allows tracking of the resultant cargo''s translocation. The development of this tagged casp‐3 cytosolic reporter lays the foundation to probe caspase therapeutic properties, charge–property relationships governing successful escape, and the precise number of caspases required for apoptotic cell death.     

Differentiation of the binding of two ligands to a tetrameric protein with a single symmetric active site by 19F NMR

R67 dihydrofolate reductase (R67 DHFR) is a plasmid‐encoded enzyme that confers resistance to the antibacterial drug trimethoprim. R67 DHFR is a tetramer with a single active site that is unusual as both cofactor and substrate are recognized by symmetry‐related residues. Such promiscuity has limited our previous efforts to differentiate ligand binding by NMR. To address this problem, we incorporated fluorine at positions 4, 5, 6, or 7 of the indole rings of tryptophans 38 and 45 and characterized the spectra to determine which probe was optimal for studying ligand binding. Two resonances were observed for all apo proteins. Unexpectedly, the W45 resonance appeared broad, and truncation of the disordered N‐termini resulted in the appearance of one sharp W45 resonance. These results are consistent with interaction of the N‐terminus with W45. Binding of the cofactor broadened W38 for all fluorine probes, whereas substrate, dihydrofolate, binding resulted in the appearance of three new resonances for 4‐ and 5‐fluoroindole labeled protein and severe line broadening for 6‐ and 7‐fluoroindole R67 DHFR. W45 became slightly broader upon ligand binding. With only two peaks in the ¹⁹F NMR spectra, our data were able to differentiate cofactor and substrate binding to the single, symmetric active site of R67 DHFR and yield binding affinities.     

EDock‐ML: A web server for using ensemble docking with machine learning to aid drug discovery

EDock‐ML is a web server that facilitates the use of ensemble docking with machine learning to help decide whether a compound is worthwhile to be considered further in a drug discovery process. Ensemble docking provides an economical way to account for receptor flexibility in molecular docking. Machine learning improves the use of the resulting docking scores to evaluate whether a compound is likely to be useful. EDock‐ML takes a bottom‐up approach in which machine‐learning models are developed one protein at a time to improve predictions for the proteins included in its database. Because the machine‐learning models are intended to be used without changing the docking and model parameters with which the models were trained, novice users can use it directly without worrying about what parameters to choose. A user simply submits a compound specified by an ID from the ZINC database (Sterling, T.; Irwin, J. J., J Chem Inf Model 2015, 55[11], 2,324–2,337.) or upload a file prepared by a chemical drawing program and receives an output helping the user decide the likelihood of the compound to be active or inactive for a drug target. EDock‐ML can be accessed freely at edock‐ml.umsl.edu     

A human kinase yeast array for the identification of kinases modulating phosphorylation‐dependent protein–protein interactions

Protein kinases play an important role in cellular signaling pathways and their dysregulation leads to multiple diseases, making kinases prime drug targets. While more than 500 human protein kinases are known to collectively mediate phosphorylation of over 290,000 S/T/Y sites, the activities have been characterized only for a minor, intensively studied subset. To systematically address this discrepancy, we developed a human kinase array in Saccharomyces cerevisiae as a simple readout tool to systematically assess kinase activities. For this array, we expressed 266 human kinases in four different S. cerevisiae strains and profiled ectopic growth as a proxy for kinase activity across 33 conditions. More than half of the kinases showed an activity‐dependent phenotype across many conditions and in more than one strain. We then employed the kinase array to identify the kinase(s) that can modulate protein–protein interactions (PPIs). Two characterized, phosphorylation‐dependent PPIs with unknown kinase–substrate relationships were analyzed in a phospho‐yeast two‐hybrid assay. CK2α1 and SGK2 kinases can abrogate the interaction between the spliceosomal proteins AAR2 and PRPF8, and NEK6 kinase was found to mediate the estrogen receptor (ERα) interaction with 14‐3‐3 proteins. The human kinase yeast array can thus be used for a variety of kinase activity‐dependent readouts.     

Structural motifs in protein cores and at protein–protein interfaces are different

Structures of proteins and protein–protein complexes are determined by the same physical principles and thus share a number of similarities. At the same time, there could be differences because in order to function, proteins interact with other molecules, undergo conformations changes, and so forth, which might impose different restraints on the tertiary versus quaternary structures. This study focuses on structural properties of protein–protein interfaces in comparison with the protein core, based on the wealth of currently available structural data and new structure‐based approaches. The results showed that physicochemical characteristics, such as amino acid composition, residue–residue contact preferences, and hydrophilicity/hydrophobicity distributions, are similar in protein core and protein–protein interfaces. On the other hand, characteristics that reflect the evolutionary pressure, such as structural composition and packing, are largely different. The results provide important insight into fundamental properties of protein structure and function. At the same time, the results contribute to better understanding of the ways to dock proteins. Recent progress in predicting structures of individual proteins follows the advancement of deep learning techniques and new approaches to residue coevolution data. Protein core could potentially provide large amounts of data for application of the deep learning to docking. However, our results showed that the core motifs are significantly different from those at protein–protein interfaces, and thus may not be directly useful for docking. At the same time, such difference may help to overcome a major obstacle in application of the coevolutionary data to docking—discrimination of the intramolecular information not directly relevant to docking.     

Analysis of protein–ligand interactions from titrations and nuclear magnetic resonance relaxation dispersions

We present PLIS, a publicly available, open‐source software for the determination of protein–ligand dissociation constants that can be used to characterize biological processes or to shed light on biophysical aspects of interactions. PLIS can analyze data from titration experiments monitored by for instance fluorescence spectroscopy or from nuclear magnetic resonance relaxation dispersion experiments. In addition to analysis of experimental data, PLIS includes functionality for generation of synthetic data, useful for understanding how different parameters effect the data in order to better analyze experiments.