Biology subjects

Nussinov, R.

Publications and source records attributed to Nussinov, R..

6 recordsLinked to original sources

bioRxiv2021

The mechanism of activation of monomeric B-Raf V600E

Oncogenic mutations in the serine/threonine kinase B-Raf, particularly the V600E mutation, are frequent in cancer, making it a major drug target. Although much is known about B-Rafs active and inactive states, questions remain about the mechanism by which the protein changes between these two states. Here, we utilize molecular dynamics to investigate both wild-type and V600E B-Raf to gain mechanistic insights into the impact of the Val to Glu mutation. The results show that the wild-type and mutant follow similar activation pathways involving an extension of the activation loop and an inward motion of the C-helix. The V600E mutation, however, destabilizes the inactive state by disrupting hydrophobic interactions present in the wild-type structure while the active state is stabilized through the formation of a salt bridge between Glu600 and Lys507. Additionally, when the activation loop is extended, the C-helix is able to move between an inward and outward orientation as long as the DFG motif adopts a specific orientation. In that orientation Phe595 rotates away from the C-helix, allowing the formation of a salt bridge between Lys483 and Glu501. These mechanistic insights have implications for the development of new Raf inhibitors.

cancer biology↗

bioRxiv2021

Discovery of Latent Drivers from Double Mutations in Pan-Cancer Data Reveal their Clinical Impact

Background Transforming patient-specific molecular data into clinical decisions is fundamental to personalized medicine. Despite massive advancements in cancer genomics, to date driver mutations whose frequencies are low, and their observable transformation potential is minor have escaped identification. Yet, when paired with other mutations in cis, such latent driver mutations can drive cancer. Here, we discover potential latent driver double mutations. Method We applied a statistical approach to identify significantly co-occurring mutations in the pan-cancer data of mutation profiles of [~]80,000 tumor sequences from the TCGA and AACR GENIE databases. The components of same gene doublets were assessed as potential latent drivers. We merged the analysis of the significant double mutations with drug response data of cell lines and patient derived xenografts (PDXs). This allowed us to link the potential impact of double mutations to clinical information and discover signatures for some cancer types. Results Our comprehensive statistical analysis identified 228 same gene double mutations of which 113 mutations are cataloged as latent drivers. Oncogenic activation of a protein can be through either single or multiple independent mechanisms of action. Combinations of a driver mutation with either a driver, a weak driver, or a strong latent driver have the potential of a single gene leading to a fully activated state and high drug response rate. Tumor suppressors require higher mutational load to coincide with double mutations compared to oncogenes which implies their relative robustness to losing their functions. Evaluation of the response of cell lines and patient-derived xenograft data to drug treatment indicate that in certain genes double mutations can increase oncogenic activity, hence a better drug response (e.g. in PIK3CA), or they can promote resistance to the drugs (e.g. in EGFR). Conclusion Our comprehensive analysis of same allele double mutations in cancer genome landscapes emphasizes that interrogation of big genomic data and integration with the results of large-scale small-molecule sensitivity data can provide deep patterns that are rare; but can still result in dramatic phenotypic alterations, and provide clinical signatures for some cancer types.

systems biology↗

bioRxiv2021

The mechanism of full activation of tumor suppressor PTEN at the phosphatidylinositol-enriched membrane

Tumor suppressor PTEN, the second most highly mutated protein in cancer, dephosphorylates signaling lipid PIP3 produced by PI3Ks. Excess PIP3 promotes cell proliferation. The mechanism at the membrane of this pivotal phosphatase is unknown hindering drug discovery. Exploiting explicit solvent simulations, we tracked full-length PTEN trafficking from the cytosol to the membrane. We observed its interaction with membranes composed of zwitterionic phosphatidylcholine, anionic phosphatidylserine, and phosphoinositides, including signaling lipids PIP2 and PIP3. We tracked its moving away from the zwitterionic and getting absorbed onto anionic membrane that harbors PIP3. We followed it localizing on microdomains enriched in signaling lipids, as PI3K does, and observed PIP3 allosterically unfolding the N-terminal PIP2 binding domain, positioning it favorably for the polybasic motif interaction with PIP2. Finally, we determined PTEN catalytic action at the membrane, all in line with experimental observations, deciphering the mechanisms of how PTEN anchors to the membrane and restrains cancer.

molecular biology↗

bioRxiv2020

A deep learning framework for elucidating whole-genome chemical interaction space

Molecular interaction is the foundation of biological process. Elucidation of genome-wide binding partners of a biomolecule will address many questions in biomedicine. However, ligands of a vast number of proteins remain elusive. Existing methods mostly fail when the protein of interest is dissimilar from those with known functions or structures. We develop a new deep learning framework DISAE that incorporates biological knowledge into self-supervised learning techniques for predicting ligands of novel unannotated proteins on a genome-scale. In the rigorous benchmark studies, DISAE outperforms state-of-the-art methods by a significant margin. The interpretability analysis of DISAE suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

bioinformatics↗

bioRxiv2020

The structural basis of the oncogenic mutant K-Ras4B homodimers

Ras proteins activate their effectors through physical interactions in response to the various extracellular stimuli at the plasma membrane. Oncogenic Ras forms dimer and nanoclusters at the plasma membrane, boosting the downstream MAPK signal. It was reported that K-Ras4B can dimerize through two major interfaces: (i) the effector lobe interface, mapped to Switch I and effector binding regions; (ii) the allosteric lobe interface involving 3 and 4 helices. Recent experiments showed that constitutively active, oncogenic mutant K-Ras4BG12D dimers are enriched in the plasma membrane. Here, we perform molecular dynamics simulations of K-Ras4BG12D homodimers aiming to quantify the two major interfaces in atomic level. To examine the effect of mutations on dimerization, two double mutations, K101D/R102E on the allosteric lobe and R41E/K42D on the effector lobe interfaces were added to the K-Ras4BG12D dimer simulations. We observed that the effector lobe K-Ras4BG12D dimer is stable, while the allosteric lobe dimer alters its helical interface during the simulations, presenting multiple conformations. The K101D/R102E mutations slightly weakens the allosteric lobe interface. However, the R41E/K42D mutations disrupt the effector lobe interface. Using the homo-oligomers prediction server, we obtained trimeric, tetrameric, and pentameric complexes with the allosteric lobe K-Ras4BG12D dimers. However, the allosteric lobe dimer with the K101D/R102E mutations is not capable of generating multiple higher order structures. Our detailed interface analysis may help to develop inhibitor design targeting functional Ras dimerization and high order oligomerization at the membrane signaling platform.

biophysics↗

bioRxiv2020

Genome-wide Prediction of Small Molecule Binding to Remote Orphan Proteins Using Distilled Sequence Alignment Embedding

Endogenous or surrogate ligands of a vast number of proteins remain unknown. Identification of small molecules that bind to these orphan proteins will not only shed new light into their biological functions but also provide new opportunities for drug discovery. Deep learning plays an increasing role in the prediction of chemical-protein interactions, but it faces several challenges in protein deorphanization. Bioassay data are highly biased to certain proteins, making it difficult to train a generalizable machine learning model for the proteins that are dissimilar from the ones in the training data set. Pre-training offers a general solution to improving the model generalization, but needs incorporation of domain knowledge and customization of task-specific supervised learning. To address these challenges, we develop a novel protein pre-training method, DIstilled Sequence Alignment Embedding (DISAE), and a module-based fine-tuning strategy for the protein deorphanization. In the benchmark studies, DISAE significantly improves the generalizability and outperforms the state-of-the-art methods with a large margin. The interpretability analysis of pre-trained model suggests that it learns biologically meaningful information. We further use DISAE to assign ligands to 649 human orphan G-Protein Coupled Receptors (GPCRs) and to cluster the human GPCRome by integrating their phylogenetic and ligand relationships. The promising results of DISAE open an avenue for exploring the chemical landscape of entire sequenced genomes.

systems biology↗