Allostery in disease and in drug discovery.

Allostery is largely associated with conformational and functional transitions in individual proteins. This concept can be extended to consider the impact of conformational perturbations on cellular function and disease states. Here, we clarify the concept of allostery and how it controls physiological activities. We focus on the challenging questions of how allostery can both cause disease and contribute to development of new therapeutics. We aim to increase the awareness of the linkage between disease symptoms on the cellular level and specific aberrant allosteric actions on the molecular level and to emphasize the potential of allosteric drugs in innovative therapies.

Are Parallel Proliferation Pathways Redundant?

Are the receptor tyrosine kinase (RTK) and JAK-STAT-driven proliferation pathways 'parallel' or 'redundant'? And what about those of K-Ras4B versus N-Ras? 'Parallel' proliferation pathways accomplish a similar drug resistance outcome. Thus, are they 'redundant'? In this paper, it is argued that there is a fundamental distinction between 'parallel' and 'redundant'. Cellular proliferation pathways are influenced by the genome sequence, 3D organization and chromatin accessibility, and determined by protein availability prior to cancer emergence. In the opinion presented, if they operate the same downstream protein families, they are redundant; if evolutionary-independent, they are parallel. Thus, RTK and JAK-STAT-driven proliferation pathways are parallel; those of Ras isoforms are redundant. Our Precision Medicine Call to map cancer proliferation pathways is vastly important since it can expedite effective therapeutics.

HMI-PRED 2.0: a biologist-oriented web application for prediction of host-microbe protein-protein interaction by interface mimicry.

SUMMARY: HMI-PRED 2.0 is a publicly available web service for the prediction of host-microbe protein-protein interaction by interface mimicry that is intended to be used without extensive computational experience. A microbial protein structure is screened against a database covering the entire available structural space of complexes of known human proteins. AVAILABILITY AND IMPLEMENTATION: HMI-PRED 2.0 provides user-friendly graphic interfaces for predicting, visualizing and analyzing host-microbe interactions. HMI-PRED 2.0 is available at https://hmipred.org/.

SARS-CoV-2 Interactome 3D: A Web interface for 3D visualization and analysis of SARS-CoV-2-human mimicry and interactions.

SUMMARY: We present a web-based server for navigating and visualizing possible interactions between SARS-CoV-2 and human host proteins. The interactions are obtained from HMI_Pred which relies on the rationale that virus proteins mimic host proteins. The structural alignment of the viral protein with one side of the human protein-protein interface determines the mimicry. The mimicked human proteins and predicted interactions, and the binding sites are presented. The user can choose one of the 18 SARS-CoV-2 protein structures and visualize the potential 3D complexes it forms with human proteins. The mimicked interface is also provided. The user can superimpose two interacting human proteins in order to see whether they bind to the same site or different sites on the viral protein. The server also tabulates all available mimicked interactions together with their match scores and number of aligned residues. This is the first server listing and cataloging all interactions between SARS-CoV-2 and human protein structures, enabled by our innovative interface mimicry strategy. AVAILABILITY AND IMPLEMENTATION: The server is available at https://interactome.ku.edu.tr/sars/.

The structural basis of BCR-ABL recruitment of GRB2 in chronic myelogenous leukemia.

BCR-ABL drives chronic myeloid leukemia (CML). BCR binding to GRB2 transduces signaling via the Ras/MAPK pathway. Despite considerable data confirming the binding, molecular-level understanding of exactly how the two proteins interact, and, especially, what are the determinants of the specificity of the SH2GRB2 domain-phosphorylated BCR (pBCR) recognition are still open questions. Yet, this is vastly important for understanding binding selectivity, and for predicting the phosphorylated receptors, or peptides, that are likely to bind. Here, we uncover these determinants and ascertain to what extent they relate to the affinity of the interaction. Toward this end, we modeled the complexes of the pBCR and SH2GRB2 and other pY/Y-peptide-SH2 complexes and compared their specificity and affinity. We observed that pBCR's 176FpYVNV180 motif is favorable and specific to SH2GRB2, similar to pEGFR, but not other complexes. SH2GRB2 contains two binding pockets: pY-binding recognition pocket triggers binding, and the specificity pocket whose interaction is governed by N179 in pBCR and W121 in SH2GRB2. Our proposed motif with optimal affinity to SH2GRB2 is E/D-pY-E/V-N-I/L. Collectively, we provide the structural basis of BCR-ABL recruitment of GRB2, outline its specificity hallmarks, and delineate a blueprint for prediction of BCR-binding scaffolds and for therapeutic peptide design.

Mechanism of activation and the rewired network: New drug design concepts.

Precision oncology benefits from effective early phase drug discovery decisions. Recently, drugging inactive protein conformations has shown impressive successes, raising the cardinal questions of which targets can profit and what are the principles of the active/inactive protein pharmacology. Cancer driver mutations have been established to mimic the protein activation mechanism. We suggest that the decision whether to target an inactive (or active) conformation should largely rest on the protein mechanism of activation. We next discuss the recent identification of double (multiple) same-allele driver mutations and their impact on cell proliferation and suggest that like single driver mutations, double drivers also mimic the mechanism of activation. We further suggest that the structural perturbations of double (multiple) in cis mutations may reveal new surfaces/pockets for drug design. Finally, we underscore the preeminent role of the cellular network which is deregulated in cancer. Our structure-based review and outlook updates the traditional Mechanism of Action, informs decisions, and calls attention to the intrinsic activation mechanism of the target protein and the rewired tumor-specific network, ushering innovative considerations in precision medicine.

Anticancer drug resistance: An update and perspective.

Driver mutations promote initiation and progression of cancer. Pharmacological treatment can inhibit the action of the mutant protein; however, drug resistance almost invariably emerges. Multiple studies revealed that cancer drug resistance is based upon a plethora of distinct mechanisms. Drug resistance mutations can occur in the same protein or in different proteins; as well as in the same pathway or in parallel pathways, bypassing the intercepted signaling. The dilemma that the clinical oncologist is facing is that not all the genomic alterations as well as alterations in the tumor microenvironment that facilitate cancer cell proliferation are known, and neither are the alterations that are likely to promote metastasis. For example, the common KRasG12C driver mutation emerges in different cancers. Most occur in NSCLC, but some occur, albeit to a lower extent, in colorectal cancer and pancreatic ductal carcinoma. The responses to KRasG12C inhibitors are variable and fall into three categories, (i) new point mutations in KRas, or multiple copies of KRAS G12C which lead to higher expression level of the mutant protein; (ii) mutations in genes other than KRAS; (iii) original cancer transitioning to other cancer(s). Resistance to adagrasib, an experimental antitumor agent exerting its cytotoxic effect as a covalent inhibitor of the G12C KRas, indicated that half of the cases present multiple KRas mutations as well as allele amplification. Redundant or parallel pathways included MET amplification; emerging driver mutations in NRAS, BRAF, MAP2K1, and RET; gene fusion events in ALK, RET, BRAF, RAF1, and FGFR3; and loss-of-function mutations in NF1 and PTEN tumor suppressors. In the current review we discuss the molecular mechanisms underlying drug resistance while focusing on those emerging to common targeted cancer drivers. We also address questions of why cancers with a common driver mutation are unlikely to evolve a common drug resistance mechanism, and whether one can predict the likely mechanisms that the tumor cell may develop. These vastly important and tantalizing questions in drug discovery, and broadly in precision medicine, are the focus of our present review. We end with our perspective, which calls for target combinations to be selected and prioritized with the help of the emerging massive compute power which enables artificial intelligence, and the increased gathering of data to overcome its insatiable needs.

Autoinhibition can identify rare driver mutations and advise pharmacology.

Identification of protein mutations that drive cancer is a major challenge. A primary reason is that driver mutations are principally identified by their high frequency even though they can also be rare. Driver mutations can locate at functional (binding or active) sites. We dub these orthosteric drivers. However, often they are allosteric drivers. Identification is particularly formidable for rare allosteric drivers. Autoinhibition, where a segment of the protein covers its functional site, is a common allosteric regulation mechanism. A modest shift in the equilibrium can switch the system from the autoinhibited to the active state. This can suggest why (i) mutations are likely to evolve to target it; (ii) inhibitors can straightforwardly relieve the autoinhibition but not vice versa; and why (iii) mutations that relieve the autoinhibition are likely to be drivers-even if they are rare. We explain in simple terms the linkage between allosteric driver mutations, release of autoinhibition, free energy landscapes, and targeted pharmacology in precision medicine. We review the literature and propose new concepts in identification of rare drivers in this framework.

Is Nanoclustering essential for all oncogenic KRas pathways? Can it explain why wild-type KRas can inhibit its oncogenic variant?

Membrane-anchored oncogenic KRas can dimerize, form nanoclusters, and signal through the MAPK (Raf/MEK/ERK) and PI3Kα/Akt/mTOR. Both pathways are needed in KRAS-driven proliferation. Here we ask: Is oncogenic KRas nanoclustering (or dimerization) essential for all KRas signaling pathways? Raf kinase domain dimerization, thus MAPK activation, requires KRas nanoclusters. By contrast, the PI3Kα heterodimer acts as a monomeric unit; thus, does PI3Kα activation and PI3Kα/Akt/mTOR signaling require nanoclustering? Further, calmodulin binds only to oncogenic KRas4B. Here we ask: Does calmodulin downregulate KRas4B cancer development as suggested early on, or promote it? We also ask: Why is oncogenic KRas4B the most abundant isoform? Does wild-type Ras indeed inhibit its oncogenic variants as data appeared to suggest? And related to the last question, why is wild-type KRas a more potent inhibitor of its oncogenic form than wild-type NRas of its oncogenic form? Resolving these cardinal questions, and others, such as how exactly does RASSF5 (NORE1A) act as tumor suppressor, and why Ras isoforms tend to occur in distinct cancer types are crucial for effective pharmacology. In this review, we take a nanoclustering/dimerization-centric outlook and show that many questions can be explained by simply considering Ras nanoclustering.

Oncogenic KRas mobility in the membrane and signaling response.

Ras signaling initiates at the plasma membrane. Thus, Ras behavior at the membrane and how it relates to its interactions with Raf and PI3Kα, are of immense interest. Here we review factors influencing Ras lateral diffusion. We then ask whether oncogenic Ras diffusion speed in the membrane is important for signaling response times and whether it affects ubiquitously all pathways. We suggest that if Ras expression is sufficiently high to dimerize (or form nanoclusters), signaling response of those pathways where dimers (or nanoclusters) are involved corresponds to the speed with which Ras molecules travel in the membrane. On average, the faster the rate at which Ras travels to dimerize, the shorter the time to MAPK signaling; but not PI3Kα. However, we argue that KRas speed may not play an important functional role because changes in mobility at this scale are unlikely to be significant. In line with this, despite the anchors' variability, lateral diffusion speeds of KRas and HRas are similar, as is that of Lck kinase; however, even though with similar anchor, Cdc42 mobility presents a different pattern, commensurate with its role in the positioning of the apical domain, suggesting that mobility evolved for function.

Protein ensembles link genotype to phenotype.

Classically, phenotype is what is observed, and genotype is the genetic makeup. Statistical studies aim to project phenotypic likelihoods of genotypic patterns. The traditional genotype-to-phenotype theory embraces the view that the encoded protein shape together with gene expression level largely determines the resulting phenotypic trait. Here, we point out that the molecular biology revolution at the turn of the century explained that the gene encodes not one but ensembles of conformations, which in turn spell all possible gene-associated phenotypes. The significance of a dynamic ensemble view is in understanding the linkage between genetic change and the gained observable physical or biochemical characteristics. Thus, despite the transformative shift in our understanding of the basis of protein structure and function, the literature still commonly relates to the classical genotype-phenotype paradigm. This is important because an ensemble view clarifies how even seemingly small genetic alterations can lead to pleiotropic traits in adaptive evolution and in disease, why cellular pathways can be modified in monogenic and polygenic traits, and how the environment may tweak protein function.

Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.

At the root of the so-called precision medicine or precision oncology, which is our focus here, is the hypothesis that cancer treatment would be considerably better if therapies were guided by a tumor's genomic alterations. This hypothesis has sparked major initiatives focusing on whole-genome and/or exome sequencing, creation of large databases, and developing tools for their statistical analyses-all aspiring to identify actionable alterations, and thus molecular targets, in a patient. At the center of the massive amount of collected sequence data is their interpretations that largely rest on statistical analysis and phenotypic observations. Statistics is vital, because it guides identification of cancer-driving alterations. However, statistics of mutations do not identify a change in protein conformation; therefore, it may not define sufficiently accurate actionable mutations, neglecting those that are rare. Among the many thematic overviews of precision oncology, this review innovates by further comprehensively including precision pharmacology, and within this framework, articulating its protein structural landscape and consequences to cellular signaling pathways. It provides the underlying physicochemical basis, thereby also opening the door to a broader community.

Correction: Review: Precision medicine and driver mutations: Computational methods, functional assays and conformational principles for interpreting cancer drivers.

[This corrects the article DOI: 10.1371/journal.pcbi.1006658.].

Emerging Allosteric Mechanism of EGFR Activation in Physiological and Pathological Contexts.

Protein kinases are key players in signal transduction pathways where they are crafted into two functional states. In response to growth factor binding stimulus, epidermal growth factor receptor (EGFR), which is physiologically populated in an autoinhibited inactive state, becomes active. Here, we outline a simple allostery scheme to clarify how an extracellular (ligand-dependent) binding event activates the intracellular EGFR kinase domain via (dimer-dependent) asymmetric dimerization, as well as how pathologically overexpressed EGFR or constitutively active mutants, leads to oncogenic pathway activation. Our underlying allosteric activation mechanism derives from a collection of inactive versus active EGFR structural, biochemical (negatively cooperative ligand binding), and biophysical (weak coupling between extracellular and intracellular kinase dimerization) data. The emerging structural insight reveals that ligand-dependent physiological activation is an outside-in allosteric activation with strong structural coupling across the membrane. In contrast, ligand-independent pathological activation is a weak inside-out activation mediated by intracellular kinase dimerization, which is structurally accommodated by additional extracellular dimers.

Dynamic Protein Allosteric Regulation and Disease.

Allostery is largely associated with conformational and functional transitions in individual proteins. All dynamic proteins are allosteric. This concept can be extended to consider the impact of conformational perturbations on cellular function and disease states. In this section, we will illuminate how allostery can control physiological activities and cause disease, aiming to increase the awareness of the linkage between disease symptoms on the cellular level and specific aberrant allosteric actions on the molecular level.

Computational Structural Biology: Successes, Future Directions, and Challenges.

Computational biology has made powerful advances. Among these, trends in human health have been uncovered through heterogeneous 'big data' integration, and disease-associated genes were identified and classified. Along a different front, the dynamic organization of chromatin is being elucidated to gain insight into the fundamental question of genome regulation. Powerful conformational sampling methods have also been developed to yield a detailed molecular view of cellular processes. when combining these methods with the advancements in the modeling of supramolecular assemblies, including those at the membrane, we are finally able to get a glimpse into how cells' actions are regulated. Perhaps most intriguingly, a major thrust is on to decipher the mystery of how the brain is coded. Here, we aim to provide a broad, yet concise, sketch of modern aspects of computational biology, with a special focus on computational structural biology. We attempt to forecast the areas that computational structural biology will embrace in the future and the challenges that it may face. We skirt details, highlight successes, note failures, and map directions.

Pathogen mimicry of host protein-protein interfaces modulates immunity.

Signaling pathways shape and transmit the cell's reaction to its changing environment; however, pathogens can circumvent this response by manipulating host signaling. To subvert host defense, they beat it at its own game: they hijack host pathways by mimicking the binding surfaces of host-encoded proteins. For this, it is not necessary to achieve global protein homology; imitating merely the interaction surface is sufficient. Different protein folds often interact via similar protein-protein interface architectures. This similarity in binding surfaces permits the pathogenic protein to compete with a host target protein. Thus, rather than binding a host-encoded partner, the host protein hub binds the pathogenic surrogate. The outcome can be dire: rewiring or repurposing the host pathways, shifting the cell signaling landscape and consequently the immune response. They can also cause persistent infections as well as cancer by modulating key signaling pathways, such as those involving Ras. Mapping the rewired host-pathogen 'superorganism' interaction network - along with its structural details - is critical for in-depth understanding of pathogenic mechanisms and developing efficient therapeutics. Here, we overview the role of molecular mimicry in pathogen host evasion as well as types of molecular mimicry mechanisms that emerged during evolution.

Oncogenic KRAS signaling and YAP1/ß-catenin: Similar cell cycle control in tumor initiation.

Why are YAP1 and c-Myc often overexpressed (or activated) in KRAS-driven cancers and drug resistance? Here, we propose that there are two independent pathways in tumor proliferation: one includes MAPK/ERK and PI3K/A kt/mTOR; and the other consists of pathways leading to the expression (or activation) of YAP1 and c-Myc. KRAS contributes through the first. MYC is regulated by e.g. ß-catenin, Notch and Hedgehog. We propose that YAP1 and ERK accomplish similar roles in cell cycle control, as do ß-catenin and PI3K. This point is compelling, since the question of how YAP1 rescues K-Ras or B-Raf ablation has recently captured much attention, as well as the mechanism of resistance to PI3K inhibitors. The similarity in cell cycle actions of ß-catenin and PI3K can also clarify the increased aggressiveness of lung cancer when both K-Ras and ß-catenin operate. Thus, we propose that the two pathways can substitute one another - or together amplify each other - in promoting proliferation. This new understanding of the independence and correspondence of the two pathways in cancer - MAPK/ERK and PI3K/Akt/mTOR; and YAP1 and c-Myc - provide a coherent and significant picture of signaling-driven oncogenic proliferation and may help in judicious, pathway-based drug discovery.

Allostery modulates the beat rate of a cardiac pacemaker.

The S672R mutation in heart cell ion channels leads to low heart rates and arrhythmia by an unknown route. A multifaceted NMR analysis now demonstrates that this mutant impacts allosteric coupling in domains inside of the cell to change channel activation, providing a mechanistic explanation for phenotypic outcomes.

The design of covalent allosteric drugs.

A key issue in drug discovery is how to reduce drug dosage and increase specificity while retaining or increasing efficacy, as high dosage is often linked to toxicity. There are two types of drugs on the market: orthosteric and allosteric. Orthosteric drugs can be noncovalent or covalent. The latter are advantageous because they may be prescribed in lower doses, but their potential off-target toxicity is a primary concern. The chief advantages of allosteric drugs are their higher specificity and their consequently lower chance of toxic side effects. Covalent allosteric drugs combine the pharmacological merits of covalent drugs with the additional benefit of the higher specificity of allosteric drugs. In a recent promising step in therapeutic drug development, allosteric, disulfide-tethered fragments successfully modulated the activity of a protein kinase and K-Ras.