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The role of TIRAP (toll/interleukin-1 receptor (TIR) domain-containing adapter protein) in macrophage inflammatory signalling has been significantly evolved since its discovery in 2001 due to its dynamic nature and subcellular localization to regulate multiple signaling through several protein–protein interactions (PPIs). Structural analysis of these interactions can reveal a better understanding of their conformational dynamics and the nature of their binding. Tyrosine phosphorylation in the TIR domain of TIRAP is very critical for its function. In toll-like receptor (TLR) 4/2 signalling, Bruton's tyrosine kinase (BTK) and Protein kinase C delta (PKCδ) are known to phosphorylate the Y86, Y106, Y159, and Y187 of TIRAP which is crucial for the downstream function of MAPKs (mitogen-activated protein kinases) activation. The objective of this study is to understand the interaction of TIRAP with p38 MAPK through molecular docking and identify the importance of TIRAP tyrosine phosphorylation in p38 MAPK interaction. In this structural study, we performed an in-silico molecular docking using HADDOCK 2.4, pyDockWEB, ClusPro 2.0, and ZDOCK 3.0.2 tools to unravel the interaction between TIRAP and p38 MAPK. Further, manual in-silico phosphorylations of TIRAP tyrosines; Y86, Y106, Y159, and Y187 was created in the Discovery Studio tool to study the conformational changes in protein docking and their binding affinities with p38 MAPK in comparison to non-phosphorylated state. Our molecular docking and 500 ns of molecular dynamic (MD) simulation study demonstrates that the Y86 phosphorylation (pY86) in TIRAP is crucial in promoting the higher binding affinity (∆G bind ) with p38 MAPK. The conformational changes due to the tyrosine phosphorylation mainly at the Y86 site pull the TIRAP closer to the active site in the kinase domain of p38 MAPK and plays a significant role at the interface site which is reversed in its dephosphorylated state. The heatmap of interactions between the TIRAP and p38 MAPK after the MD simulation shows that the TIRAP pY86 structure makes the highest number of stable hydrogen bonds with p38 MAPK residues. Our findings may further be validated in an in-vitro system and would be crucial for targeting the TIRAP and p38 MAPK interaction for therapeutic purposes against the chronic inflammatory response and associated diseases.

Conformational sampling is one of the bottlenecks in fragment-based protein structure prediction approaches. They generally start with a coarse-grained optimization where mainchain atoms and centroids of side chains are considered, followed by a fine-grained optimization with an all-atom representation of proteins. It is during this coarse-grained phase that fragment-based methods sample intensely the conformational space. If the native-like region is sampled more, the accuracy of the final all-atom predictions may be improved accordingly. In this work we present EdaFold, a new method for fragment-based protein structure prediction based on an Estimation of Distribution Algorithm. Fragment-based approaches build protein models by assembling short fragments from known protein structures. Whereas the probability mass functions over the fragment libraries are uniform in the usual case, we propose an algorithm that learns from previously generated decoys and steers the search toward native-like regions. A comparison with Rosetta AbInitio protocol shows that EdaFold is able to generate models with lower energies and to enhance the percentage of near-native coarse-grained decoys on a benchmark of [Formula: see text] proteins. The best coarse-grained models produced by both methods were refined into all-atom models and used in molecular replacement. All atom decoys produced out of EdaFold's decoy set reach high enough accuracy to solve the crystallographic phase problem by molecular replacement for some test proteins. EdaFold showed a higher success rate in molecular replacement when compared to Rosetta. Our study suggests that improving low resolution coarse-grained decoys allows computational methods to avoid subsequent sampling issues during all-atom refinement and to produce better all-atom models. EdaFold can be downloaded from http://www.riken.jp/zhangiru/software.html [corrected].

One of the underlying principles in drug discovery is that a biologically active compound is complimentary in shape and molecular recognition features to its receptor. This principle infers that molecules binding to the same receptor may share some common features. Here, we have investigated whether the electrostatic similarity can be used for the discovery of small molecule protein-protein interaction inhibitors (SMPPIIs). We have developed a method that can be used to evaluate the similarity of electrostatic potentials between small molecules and known protein ligands. This method was implemented in a software called EleKit. Analyses of all available (at the time of research) SMPPII structures indicate that SMPPIIs bear some similarities of electrostatic potential with the ligand proteins of the same receptor. This is especially true for the more polar SMPPIIs. Retrospective analysis of several successful SMPPIIs has shown the applicability of EleKit in the design of new SMPPIIs.

The ongoing evolution of severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2) has resulted in the recent emergence of a highly divergent variant of concern (VOC) defined as Omicron or B.1.1.529. This VOC is of particular concern because it has the potential to evade most therapeutic antibodies and has undergone a sustained genetic evolution, resulting in the emergence of five distinct sub-lineages. However, the evolutionary dynamics of the initially identified Omicron BA.1 and BA.2 sub-lineages remain poorly understood. Herein, we combined Bayesian phylogenetic analysis, mutational profiling, and selection pressure analysis to track the virus’s genetic changes that drive the early evolutionary dynamics of the Omicron. Based on the Omicron dataset chosen for the improved temporal signals and sampled globally between November 2021 and January 2022, the most recent common ancestor (tMRCA) and substitution rates for BA.1 were estimated to be that of 18 September 2021 (95% highest posterior density (HPD), 4 August–22 October 2021) and 1.435 × 10−3 (95% HPD  =  1.021 × 10−3 − 1.869 × 10−3) substitution/site/year, respectively, whereas 3 November 2021 (95% highest posterior density (HPD) 26 September–28 November 2021) and 1.074 × 10−3 (95% HPD  =  6.444 × 10−4 − 1.586 × 10−3) substitution/site/year were estimated for the BA.2 sub-lineage. The findings of this study suggest that the Omicron BA.1 and BA.2 sub-lineages originated independently and evolved over time. Furthermore, we identified multiple sites in the spike protein undergoing continued diversifying selection that may alter the neutralization profile of BA.1. This study sheds light on the ongoing global genomic surveillance and Bayesian molecular dating analyses to better understand the evolutionary dynamics of the virus and, as a result, mitigate the impact of emerging variants on public health.

Fragment assembly is a powerful method of protein structure prediction that builds protein models from a pool of candidate fragments taken from known structures. Stochastic sampling is subsequently used to refine the models. The structures are first represented as coarse-grained models and then as all-atom models for computational efficiency. Many models have to be generated independently due to the stochastic nature of the sampling methods used to search for the global minimum in a complex energy landscape. In this paper we present EdaFold(AA), a fragment-based approach which shares information between the generated models and steers the search towards native-like regions. A distribution over fragments is estimated from a pool of low energy all-atom models. This iteratively-refined distribution is used to guide the selection of fragments during the building of models for subsequent rounds of structure prediction. The use of an estimation of distribution algorithm enabled EdaFold(AA) to reach lower energy levels and to generate a higher percentage of near-native models. [Formula: see text] uses an all-atom energy function and produces models with atomic resolution. We observed an improvement in energy-driven blind selection of models on a benchmark of EdaFold(AA) in comparison with the [Formula: see text] AbInitioRelax protocol.

Inflammation and cancer, two therapeutic areas historically addressed by separate drug discovery efforts, are now coupled in treatment approaches by a growing understanding of the dynamic molecular dialogues between immune and cancer cells. Agents that target specific compartments of the immune system, therefore, not only bring new disease modifying modalities to inflammatory diseases, but also offer a new avenue to cancer therapy by disrupting immune components of the microenvironment that foster tumor growth, progression, immune evasion, and treatment resistance. McDonough feline sarcoma viral (v-fms) oncogene homolog (FMS) and v-kit Hardy-Zuckerman 4 feline sarcoma viral oncogene homolog (KIT) are two hematopoietic cell surface receptors that regulate the development and function of macrophages and mast cells, respectively. We disclose a highly specific dual FMS and KIT kinase inhibitor developed from a multifaceted chemical scaffold. As expected, this inhibitor blocks the activation of macrophages, osteoclasts, and mast cells controlled by these two receptors. More importantly, the dual FMS and KIT inhibition profile has translated into a combination of benefits in preclinical disease models of inflammation and cancer.

In the present study, we report a human-inherited, impaired, adaptive immunity disorder, which predominantly manifested as a B cell differentiation defect, caused by a heterozygous IKZF3 missense variant, resulting in a glycine-to-arginine replacement within the DNA-binding domain of the encoded AIOLOS protein. Using mice that bear the corresponding variant and recapitulate the B and T cell phenotypes, we show that the mutant AIOLOS homodimers and AIOLOS–IKAROS heterodimers did not bind the canonical AIOLOS–IKAROS DNA sequence. In addition, homodimers and heterodimers containing one mutant AIOLOS bound to genomic regions lacking both canonical motifs. However, the removal of the dimerization capacity from mutant AIOLOS restored B cell development. Hence, the adaptive immunity defect is caused by the AIOLOS variant hijacking IKAROS function. Heterodimeric interference is a new mechanism of autosomal dominance that causes inborn errors of immunity by impairing protein function via the mutation of its heterodimeric partner. The zinc-finger transcription factor IKAROS is essential for B cell development. Taniuchi, Morio and colleagues identify a human kindred presenting with B cell immunodeficiency that was caused by a heterozygous missense mutation in IKZF3 encoding the related AIOLOS protein. AIOLOS G159R is a mutant protein that interferes with both wild-type AIOLOS and IKAROS by forming heterodimers that bind to aberrant DNA-binding sites and prevent normal expression of IKAROS-dependent genes.

Several mutations in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) have increased the transmission and mortality rate of coronavirus disease-19 (COVID-19) across the globe. Although many vaccines have been developed, a large proportion of the global population remains at high risk of infection. The current study aims to develop an antiviral peptide capable of inhibiting the interaction of SARS-CoV-2 spike protein and its six major variants with the host cell angiotensin-converting enzyme 2 (ACE2) receptor. An in-silico approach was employed to design a therapeutic peptide inhibitor against the receptor-binding domain (RBD) of the spike (S) protein of SARS-CoV-2 and its variants (B.1.1.7, B.1.351, P.1, B.1.617.1, B.1.617.2 and B.1.617.3). The binding specificity and affinity of our designed peptide inhibitor Mod13AApi (YADKYQKQYKDAY) with wild-type S-RBD and its six variants was confirmed by molecular docking using the HPEPDOCK tool, whereas complex stability was determined by the MD simulation study. The physicochemical and ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of inhibitory peptides were determined using the ExPASy tool and pkCSM server. The docking results and its properties from our in-silico analysis present the Mod13AApi, a promising peptide for the rapid development of anti-coronavirus peptide-based antiviral therapy. Blockage of the binding of the spike protein of SARS-CoV-2 variants with ACE2 in the presence of the therapeutic peptide may prevent deadly SARS-CoV-2 variants entry into host cells. Therefore, the designed inhibitory peptide can be utilized as a promising therapeutic strategy to combat COVID-19, as evident from this in-silico study.

Targeted anticancer drug hope for melanoma PLX4032, a small-molecule inhibitor being developed by Plexxikon of California and Roche Pharmaceuticals in New Jersey ( http://go.nature.com/QnVGQx ), selectively targets B-RAFV600E, a mutant form of the B-RAF protein kinase common in several human cancers. In this issue of Nature , Gideon Bollag and colleagues report promising results for PLX4032 in an early clinical trial in melanoma patients who carry this B-RAF mutation. They also describe the structure and function of PLX4032 and present translational data from a phase I trial to show that clinical efficacy requires a drug concentration that is sufficient to cause a substantial degree of inhibition of the ERK pathway downstream of B-RAF. The study demonstrates how the design of early clinical trials based on the biological mechanisms underlying tumour formation has the potential to speed up the process by which anticancer drugs can reach the clinic. PLX4032 is a selective inhibitor of the B-RAF protein that has shown promising results in an early clinical trial in melanoma patients with an activating mutation in B-RAF. Now the structure and function of this inhibitor are described. Translational data from a phase I trial show that clinical efficacy requires a substantial degree of inhibition of the ERK pathway downstream of B-RAF. The data also show that BRAF -mutant melanomas are highly dependent on B-RAF activity. B-RAF is the most frequently mutated protein kinase in human cancers 1 . The finding that oncogenic mutations in BRAF are common in melanoma 2 , followed by the demonstration that these tumours are dependent on the RAF/MEK/ERK pathway 3 , offered hope that inhibition of B-RAF kinase activity could benefit melanoma patients. Herein, we describe the structure-guided discovery of PLX4032 (RG7204), a potent inhibitor of oncogenic B-RAF kinase activity. Preclinical experiments demonstrated that PLX4032 selectively blocked the RAF/MEK/ERK pathway in BRAF mutant cells and caused regression of BRAF mutant xenografts 4 . Toxicology studies confirmed a wide safety margin consistent with the high degree of selectivity, enabling Phase 1 clinical trials using a crystalline formulation of PLX4032 (ref. 5 ). In a subset of melanoma patients, pathway inhibition was monitored in paired biopsy specimens collected before treatment initiation and following two weeks of treatment. This analysis revealed substantial inhibition of ERK phosphorylation, yet clinical evaluation did not show tumour regressions. At higher drug exposures afforded by a new amorphous drug formulation 4 , 5 , greater than 80% inhibition of ERK phosphorylation in the tumours of patients correlated with clinical response. Indeed, the Phase 1 clinical data revealed a remarkably high 81% response rate in metastatic melanoma patients treated at an oral dose of 960 mg twice daily 5 . These data demonstrate that BRAF -mutant melanomas are highly dependent on B-RAF kinase activity.

Molecular docking predicts the best pose of a ligand in the target protein binding site by sampling and scoring numerous conformations and orientations of the ligand. Failures in pose prediction are often due to either insufficient sampling or scoring function errors. To improve the accuracy of pose prediction by tackling the sampling problem, we have developed a method of pose prediction using shape similarity. It first places a ligand conformation of the highest 3D shape similarity with known crystal structure ligands into protein binding site and then refines the pose by repacking the side-chains and performing energy minimization with a Monte Carlo algorithm. We have assessed our method utilizing CSARdock 2012 and 2014 benchmark exercise datasets consisting of co-crystal structures from eight proteins. Our results revealed that ligand 3D shape similarity could substitute conformational and orientational sampling if at least one suitable co-crystal structure is available. Our method identified poses within 2 Å RMSD as the top-ranking pose for 85.7 % of the test cases. The median RMSD for our pose prediction method was found to be 0.81 Å and was better than methods performing extensive conformational and orientational sampling within target protein binding sites. Furthermore, our method was better than similar methods utilizing ligand 3D shape similarity for pose prediction.