Mapping the Conformation Space of Wildtype and Mutant H-Ras with a Memetic, Cellular, and Multiscale Evolutionary Algorithm

Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review

Recent breakthroughs in deep learning have revolutionized protein sequence and structure prediction. These advancements are built on decades of protein design efforts, and are overcoming traditional time and cost limitations. Diffusion models, at the forefront of these innovations, significantly enhance design efficiency by automating knowledge acquisition. In the field of de novo protein design, the goal is to create entirely novel proteins with predetermined structures. Given the arbitrary positions of proteins in 3-D space, graph representations and their properties are widely used in protein generation studies. A critical requirement in protein modelling is maintaining spatial relationships under transformations (rotations, translations, and reflections). This property, known as equivariance, ensures that predicted protein characteristics adapt seamlessly to changes in orientation or position. Equivariant graph neural networks offer a solution to this challenge. By incorporating equivariant graph neural networks to learn the score of the probability density function in diffusion models, one can generate proteins with robust 3-D structural representations. This review examines the latest deep learning advancements, specifically focusing on frameworks that combine diffusion models with equivariant graph neural networks for protein generation.

Molecular dynamics simulations for the structure-based drug design: targeting small-GTPases proteins

INTRODUCTION

Molecular Dynamics (MD) simulations can support mechanism-based drug design. Indeed, MD simulations by capturing biomolecule motions at finite temperatures can reveal hidden binding sites, accurately predict drug-binding poses, and estimate the thermodynamics and kinetics, crucial information for drug discovery campaigns. Small-Guanosine Triphosphate Phosphohydrolases (GTPases) regulate a cascade of signaling events, that affect most cellular processes. Their deregulation is linked to several diseases, making them appealing drug targets. The broad roles of small-GTPases in cellular processes and the recent approval of a covalent KRas inhibitor as an anticancer agent renewed the interest in targeting small-GTPase with small molecules.

AREA COVERED

This review emphasizes the role of MD simulations in elucidating small-GTPase mechanisms, assessing the impact of cancer-related variants, and discovering novel inhibitors.

EXPERT OPINION

The application of MD simulations to small-GTPases exemplifies the role of MD simulations in the structure-based drug design process for challenging biomolecular targets. Furthermore, AI and machine learning-enhanced MD simulations, coupled with the upcoming power of quantum computing, are promising instruments to target elusive small-GTPases mutations and splice variants. This powerful synergy will aid in developing innovative therapeutic strategies associated to small-GTPases deregulation, which could potentially be used for personalized therapies and in a tissue-agnostic manner to treat tumors with mutations in small-GTPases.

Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors

Background

Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb).

Results

The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods.

Conclusions

VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications.

Availability

VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.

Elucidating the Role of Wildtype and Variant FGFR2 Structural Dynamics in (Dys)Function and Disorder

The fibroblast growth factor receptor 2 (FGFR2) gene is one of the most extensively studied genes with many known mutations implicated in several human disorders, including oncogenic ones. Most FGFR2 disease-associated gene mutations are missense mutations that result in constitutive activation of the FGFR2 protein and downstream molecular pathways. Many tertiary structures of the FGFR2 kinase domain are publicly available in the wildtype and mutated forms and in the inactive and activated state of the receptor. The current literature suggests a molecular brake inhibiting the ATP-binding A loop from adopting the activated state. Mutations relieve this brake, triggering allosteric changes between active and inactive states. However, the existing analysis relies on static structures and fails to account for the intrinsic structural dynamics. In this study, we utilize experimentally resolved structures of the FGFR2 tyrosine kinase domain and machine learning to capture the intrinsic structural dynamics, correlate it with functional regions and disease types, and enrich it with predicted structures of variants with currently no experimentally resolved structures. Our findings demonstrate the value of machine learning-enabled characterizations of structure dynamics in revealing the impact of mutations on (dys)function and disorder in FGFR2.

The switch states of the GDP-bound HRAS affected by point mutations: a study from Gaussian accelerated molecular dynamics simulations and free energy landscapes

Point mutations play a vital role in the conformational transformation of HRAS. In this work, Gaussian accelerated molecular dynamics (GaMD) simulations followed by constructions of free energy landscapes (FELs) were adopted to explore the effect of mutations D33K, A59T and L120A on conformation states of the GDP-bound HRAS. The results from the post-processing analyses on GaMD trajectories suggest that mutations alter the flexibility and motion modes of the switch domains from HRAS. The analyses from FELs show that mutations induce more disordered states of the switch domains and affect interactions of GDP with HRAS, implying that mutations yield a vital effect on the binding of HRAS to effectors. The GDP-residue interaction network revealed by our current work indicates that salt bridges and hydrogen bonding interactions (HBIs) play key roles in the binding of GDP to HRAS. Furthermore, instability in the interactions of magnesium ions and GDP with the switch SI leads to the extreme disorder of the switch domains. This study is expected to provide the energetic basis and molecular mechanism for further understanding the function of HRAS.Communicated by Ramaswamy H. Sarma.

Adaptive Stochastic Optimization to Improve Protein Conformation Sampling

We have long known that characterizing protein structures structure is key to understanding protein function. Computational approaches have largely addressed a narrow formulation of the problem, seeking to compute one native structure from an amino-acid sequence. Now AlphaFold2 is shown to be able to reveal a high-quality native structure for many proteins. However, researchers over the years have argued for broadening our view to account for the multiplicity of native structures. We now know that many protein molecules switch between different structures to regulate interactions with molecular partners in the cell. Elucidating such structures de novo is exceptionally difficult, as it requires exploration of possibly a very large structure space in search of competing, near-optimal structures. Here we report on a novel stochastic optimization method capable of revealing very different structures for a given protein from knowledge of its amino-acid sequence. The method leverages evolutionary search techniques and adapts its exploration of the search space to balance between exploration and exploitation in the presence of a computational budget. In addition to demonstrating the utility of this method for identifying multiple native structures, we additionally provide a benchmark dataset for researchers to continue work on this problem.

Molecular docking analysis of KRAS inhibitors for cancer management

The majority of human tumors are characterized by abnormal signaling caused by oncogenic RAS proteins. KRAS is a member of the RAS family and is currently one of the most thoroughly researched targets for cancer treatment due to its prevalence in a variety of deadly malignancies. Targeting the KRAS protein, which plays a crucial role in regulating cell growth, differentiation, and apoptosis, shows great potential as a strategy for fighting cancer. Herein, in silico screening of 530 natural compounds against KRAS protein was performed. The top-scoring hits, namely ZINC32502206, ZINC98363763, ZINC85645815, and ZINC98364259 displayed a robust affinity towards KRAS as evidenced by their respective binding affinity values of -10.50, -10.01, -9.80, and -9.70 kcal/mol, respectively which were notably higher than that of the control compound AMG 510 (-9.10 kcal/mol). Through virtual screening and visual inspection, it was observed that these hits effectively interacted with the essential residues located within the active site of KRAS. Based on the findings of this study, it can be inferred that these compounds may have the potential to be employed in the treatment of cancer by targeting KRAS.

Conformations and binding pockets of HRas and its guanine nucleotide exchange factors complexes in the guanosine triphosphate exchange process

The human Son of Sevenless (SOS) activates the signal-transduction protein Ras by forming the complex SOS·Ras and accelerating the guanosine triphosphate (GTP) exchange in Ras. Inhibition of SOS·Ras could regulate the function of Ras in cells and has emerged as an effective strategy for battling Ras related cancers. A key factor to the success of this approach is to understand the conformational change of Ras during the GTP exchange process. In this study, we perform an extensive molecular dynamics simulation to characterize the specific conformations of Ras without and with guanine nucleotide exchange factors (GEFs) of SOS, especially for the substates of State 1 of HRasGTP∙Mg²⁺ . The potent binding pockets on the surfaces of the RasGDP∙Mg²⁺ , the S1.1 and S1.2 substates in State 1 of RasGTP∙Mg²⁺ and the ternary complexes with SOS are predicted, including the binding sites of other domains of SOS. These findings help to obtain a more thorough understanding of Ras functions in the GTP cycling process and provide a structural foundation for future drug design.

Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma

Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and "promiscuous" interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

Structural bioinformatics enhances mechanistic interpretation of genomic variation, demonstrated through the analyses of 935 distinct RAS family mutations

MOTIVATION

Protein-coding genetic alterations are frequently observed in Clinical Genetics, but the high yield of variants of uncertain significance remains a limitation in decision making. RAS-family GTPases are cancer drivers, but only 54 variants, across all family members, fall within well-known hotspots. However, extensive sequencing has identified 881 non-hotspot variants for which significance remains to be investigated.

RESULTS

Here, we evaluate 935 missense variants from seven RAS genes, observed in cancer, RASopathies and the healthy adult population. We characterized hotspot variants, previously studied experimentally, using 63 sequence- and 3D structure-based scores, chosen by their breadth of biophysical properties. Applying scores that display best correlation with experimental measures, we report new valuable mechanistic inferences for both hot-spot and non-hotspot variants. Moreover, we demonstrate that 3D scores have little-to-no correlation with those based on DNA sequence, which are commonly used in Clinical Genetics. Thus, combined, these new knowledge bear significant relevance.

AVAILABILITY AND IMPLEMENTATION

All genomic and 3D scores, and markdown for generating figures, are provided in our supplemental data.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Generative Adversarial Learning of Protein Tertiary Structures

Protein molecules are inherently dynamic and modulate their interactions with different molecular partners by accessing different tertiary structures under physiological conditions. Elucidating such structures remains challenging. Current momentum in deep learning and the powerful performance of generative adversarial networks (GANs) in complex domains, such as computer vision, inspires us to investigate GANs on their ability to generate physically-realistic protein tertiary structures. The analysis presented here shows that several GAN models fail to capture complex, distal structural patterns present in protein tertiary structures. The study additionally reveals that mechanisms touted as effective in stabilizing the training of a GAN model are not all effective, and that performance based on loss alone may be orthogonal to performance based on the quality of generated datasets. A novel contribution in this study is the demonstration that Wasserstein GAN strikes a good balance and manages to capture both local and distal patterns, thus presenting a first step towards more powerful deep generative models for exploring a possibly very diverse set of structures supporting diverse activities of a protein molecule in the cell.

Unveiling conformational dynamics changes of H-Ras induced by mutations based on accelerated molecular dynamics

Uncovering molecular basis with regard to the conformational change of two switches I and II in the GppNHp (GNP)-bound H-Ras is highly significant for the understanding of Ras signaling. For this purpose, accelerated molecular dynamics (aMD) simulations and principal component (PC) analysis are integrated to probe the effect of mutations G12V, T35S and Q61K on conformational transformation between two switches of the GNP-bound H-Ras. The RMSF and cross-correlation analyses suggest that three mutations exert a vital effect on the flexibility and internal dynamics of two switches in the GNP-bound H-Ras. The results stemming from PC analysis indicate that two switches in the GNP-bound WT H-Ras tend to form a closed state in most conformations, while those in the GNP-bound mutated H-Ras display transformation between different states. This conclusion is further supported by free energy landscapes constructed by using the distances of residues 12 away from 35 and 35 away from 61 as reaction coordinates and different experimental studies. Interaction scanning is performed on aMD trajectories and the information shows that conformational transformations of two switches I and II induced by mutations extremely affect the GNP-residue interactions. Meanwhile, the scanning results also signify that residues G15, A18, F28, K117, A146 and K147 form stable contacts with GNP, while residues D30, E31, Y32, D33, P34 and E62 in two switches I and II produce unstable contacts with GNP. This study not only reveals dynamic behavior changes of two switches in H-Ras induced by mutations, but also unveils general principles and mechanisms with regard to functional conformational changes of H-Ras.

Evaluating Autoencoder-Based Featurization and Supervised Learning for Protein Decoy Selection

Rapid growth in molecular structure data is renewing interest in featurizing structure. Featurizations that retain information on biological activity are particularly sought for protein molecules, where decades of research have shown that indeed structure encodes function. Research on featurization of protein structure is active, but here we assess the promise of autoencoders. Motivated by rapid progress in neural network research, we investigate and evaluate autoencoders on yielding linear and nonlinear featurizations of protein tertiary structures. An additional reason we focus on autoencoders as the engine to obtain featurizations is the versatility of their architectures and the ease with which changes to architecture yield linear versus nonlinear features. While open-source neural network libraries, such as Keras, which we employ here, greatly facilitate constructing, training, and evaluating autoencoder architectures and conducting model search, autoencoders have not yet gained popularity in the structure biology community. Here we demonstrate their utility in a practical context. Employing autoencoder-based featurizations, we address the classic problem of decoy selection in protein structure prediction. Utilizing off-the-shelf supervised learning methods, we demonstrate that the featurizations are indeed meaningful and allow detecting active tertiary structures, thus opening the way for further avenues of research.

Design, synthesis, and biological evaluation of novel substituted thiourea derivatives as potential anticancer agents for NSCLC by blocking K-Ras protein-effectors interactions

Mutation of the proto-oncogene K-Ras is one of the most common molecular mechanisms in non-small cell lung cancer. Many drugs for treating lung cancer have been developed, however, due to clinical observed K-Ras mutations, corresponding chemotherapy and targeted therapy for such mutation are not efficient enough. In this study, on the basis of the crystal structure of K-Ras, 21 analogues (TKR01–TKR21) containing urea or thiourea were rationally designed, which can effectively inhibit the lung cancer cell A549 growth. The designing of these compounds was based on the structure of K-Ras protein, and the related groups were replaced by bioisosteres to improve the affinity and selectivity. Biological testing revealed that compound TKR15 could significantly inhibit the proliferation of A549 cell with IC₅₀ of 0.21 µM. Docking analysis showed that the TKR15 can effectively bind to the hydrophobic cavity and form a hydrogen bond with the Glu37. In addition, through flow apoptosis assay and immunofluorescence staining assay, it confirmed that this compound can inhibit A549 cell proliferation with the mechanism of blocking K-Ras^G12V protein and effector proteins interactions through the apoptotic pathway. In conclusion, our studies in finding novel potent compound (TKR15) with confirmed mechanism showed great potential for further optimisation and other medicinal chemistry relevant studies.

Attenuating dependence on structural data in computing protein energy landscapes

Background

Nearly all cellular processes involve proteins structurally rearranging to accommodate molecular partners. The energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. In principle, reconstructing a protein’s energy landscape holds the key to characterizing the structural dynamics and its regulation of protein function. In practice, the disparate spatio-temporal scales spanned by the slow dynamics challenge both wet and dry laboratories. However, the growing number of deposited structures for proteins central to human biology presents an opportunity to infer the relevant dynamics via exploitation of the information encoded in such structures about equilibrium dynamics.

Results

Recent computational efforts using extrinsic modes of motion as variables have successfully reconstructed detailed energy landscapes of several medium-size proteins. Here we investigate the extent to which one can reconstruct the energy landscape of a protein in the absence of sufficient, wet-laboratory structural data. We do so by integrating intrinsic modes of motion extracted off a single structure in a stochastic optimization framework that supports the plug-and-play of different variable selection strategies. We demonstrate that, while knowledge of more wet-laboratory structures yields better-reconstructed landscapes, precious information can be obtained even when only one structural model is available.

Conclusions

The presented work shows that it is possible to reconstruct the energy landscape of a protein with reasonable detail and accuracy even when the structural information about the protein is limited to one structure. By attenuating the dependence on structural data of methods designed to compute protein energy landscapes, the work opens up interesting venues of research on structure-based inference of dynamics. Of particular interest are directions of research that will extend such inference to proteins with no experimentally-characterized structures.

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.

From mutations to mechanisms and dysfunction via computation and mining of protein energy landscapes

Background

The protein energy landscape underscores the inherent nature of proteins as dynamic molecules interconverting between structures with varying energies. Reconstructing a protein’s energy landscape holds the key to characterizing a protein’s equilibrium conformational dynamics and its relationship to function. Many pathogenic mutations in protein sequences alter the equilibrium dynamics that regulates molecular interactions and thus protein function. In principle, reconstructing energy landscapes of a protein’s healthy and diseased variants is a central step to understanding how mutations impact dynamics, biological mechanisms, and function.

Results

Recent computational advances are yielding detailed, sample-based representations of protein energy landscapes. In this paper, we propose and describe two novel methods that leverage computed, sample-based representations of landscapes to reconstruct them and extract from them informative local structures that reveal the underlying organization of an energy landscape. Such structures constitute landscape features that, as we demonstrate here, can be utilized to detect alterations of landscapes upon mutation.

Conclusions

The proposed methods detect altered protein energy landscape features in response to sequence mutations. By doing so, the methods allow formulating hypotheses on the impact of mutations on specific biological activities of a protein. This work demonstrates that the availability of energy landscapes of healthy and diseased variants of a protein opens up new avenues to harness the quantitative information embedded in landscapes to summarize mechanisms via which mutations alter protein dynamics to percolate to dysfunction.

Flexibility enables to discriminate between ligands: Lessons from structural ensembles of Bcl-xl and Mcl-1

The proteins of Bcl-2 family, which are promising anti-cancer-drug targets, have substantial similarity in primary sequence and share homologous domains as well as similar structural folds. In spite of similarities in sequence and structures, the members of its pro- and anti- apoptotic subgroups form complexes with different type of partners with discriminating binding affinities. Understanding the origin of this discrimination is very important for designing ligands that can either selectively target a protein or could be made broad ranged as necessary. Using principal component analysis (PCA) of the available structures and from the analysis of the evolution of the binding pocket residues, the correlation has been investigated considering two important anti-apoptotic protein Bcl-xl and Mcl-1, which serve as two ideal representatives of this family. The flexibility of the receptor enables them to discriminate between the ligands or the binding partners. It has been observed that although Bcl-xl and Mcl-1 are classified as homologous proteins, through the course of evolution the binding pocket residues are highly conserved for Bcl-xl; whereas they have been substituted frequently in Mcl-1. The investigation has revealed that the Bcl-xl can adjust the backbone conformation of the binding pocket residues to a larger extent to complement with the shape of different binding partners whereas the Mcl-1 shows more variation in the side chain conformation of binding pocket residues for the same purpose.

From Optimization to Mapping: An Evolutionary Algorithm for Protein Energy Landscapes

Stochastic search is often the only viable option to address complex optimization problems. Recently, evolutionary algorithms have been shown to handle challenging continuous optimization problems related to protein structure modeling. Building on recent work in our laboratories, we propose an evolutionary algorithm for efficiently mapping the multi-basin energy landscapes of dynamic proteins that switch between thermodynamically stable or semi-stable structural states to regulate their biological activity in the cell. The proposed algorithm balances computational resources between exploration and exploitation of the nonlinear, multimodal landscapes that characterize multi-state proteins via a novel combination of global and local search to generate a dynamically-updated, information-rich map of a protein's energy landscape. This new mapping-oriented EA is applied to several dynamic proteins and their disease-implicated variants to illustrate its ability to map complex energy landscapes in a computationally feasible manner. We further show that, given the availability of such maps, comparison between the maps of wildtype and variants of a protein allows for the formulation of a structural and thermodynamic basis for the impact of sequence mutations on dysfunction that may prove useful in guiding further wet-laboratory investigations of dysfunction and molecular interventions.

Identification of a New Potent Inhibitor Targeting KRAS in Non-small Cell Lung Cancer Cells

KRAS (v-Ki-ras2 Kirsten rat sarcoma viral oncogene homolog) is an oncogenic driver with mutations in 30% of non-small cell lung cancer (NSCLC). However, there is no effective clinical drug even though it has been identified as an oncogene for 30 years. In this study, we identified a small molecule inhibitor compound 0375-0604 targeting KRAS by using molecular docking based virtual screening approach. Compound 0375-0604 had a good binding affinity to KRAS in vitro and exhibited cytotoxicity in oncogenic KRAS expressing NSCLC cell lines. Further mechanism study showed that compound 0375-0604 can block the formation of the complex of guanosine triphosphate (GTP) and KRAS in vitro. In addition, compound 0375-0604 inhibited KRAS downstream signaling pathway RAF/MEK/ERK and RAF/PI3K/AKT. Finally, we also found that this compound can inhibit the cell growth through G2/M cell cycle arrest and induce apoptosis on the NSCLC cell lines harboring KRAS mutation. Therefore, compound 0375-0604 may be considered as a potential KRAS inhibitor for treatment of NSCLC carrying KRAS oncogene.

Molecular Dynamics Simulations and Dynamic Network Analysis Reveal the Allosteric Unbinding of Monobody to H-Ras Triggered by R135K Mutation

Ras proteins, as small GTPases, mediate cell proliferation, survival and differentiation. Ras mutations have been associated with a broad spectrum of human cancers and thus targeting Ras represents a potential way forward for cancer therapy. A recently reported monobody NS1 allosterically disrupts the Ras-mediated signaling pathway, but its efficacy is reduced by R135K mutation in H-Ras. However, the detailed mechanism is unresolved. Here, using molecular dynamics (MD) simulations and dynamic network analysis, we explored the molecular mechanism for the unbinding of NS1 to H-Ras and shed light on the underlying allosteric network in H-Ras. MD simulations revealed that the overall structures of the two complexes did not change significantly, but the H-Ras–NS1 interface underwent significant conformational alteration in the mutant Binding free energy analysis showed that NS1 binding was unfavored after R135K mutation, which resulted in the unfavorable binding of NS1. Furthermore, the critical residues on H-Ras responsible for the loss of binding of NS1 were identified. Importantly, the allosteric networks for these important residues were revealed, which yielded a novel insight into the allosteric regulatory mechanism of H-Ras.

Microsecond molecular dynamics simulations provide insight into the ATP-competitive inhibitor-induced allosteric protection of Akt kinase phosphorylation

Akt is a serine/threonine protein kinase, a critical mediator of growth factor-induced survival in key cellular pathways. Allosteric signaling between protein intramolecular domains requires long-range communication mediated by hotspot residues, often triggered by ligand binding. Here, based on extensive 3 μs explicit solvent molecular dynamics (MD) simulations of Akt1 kinase domain in the unbound (apo) and ATP-competitive inhibitor, GDC-0068-bound states, we propose a molecular mechanism for allosteric regulation of Akt1 kinase phosphorylation by GDC-0068 binding to the ATP-binding site. MD simulations revealed that the apo Akt1 is flexible with two disengaged N- and C-lobes, equilibrated between the open and closed conformations. GDC-0068 occupancy of the ATP-binding site shifts the conformational equilibrium of Akt1 from the open conformation toward the closed conformation and stabilizes the closed state. This effect enables allosteric signal propagation from the GDC-0068 to the phosphorylated T308 (pT308) in the activation loop and restrains phosphatase access to pT308, thereby protecting the pT308 in the GDC-0068-bound Akt1. Importantly, functional hotspots involved in the allosteric communication from the GDC-0068 to the pT308 are identified. Our analysis of GDC-0068-induced allosteric protection of Akt kinase phosphorylation yields important new insights into the molecular mechanism of allosteric regulation of Akt kinase activity.

Intrinsic K-Ras dynamics: A novel molecular dynamics data analysis method shows causality between residue pair motions

K-Ras is the most frequently mutated oncogene in human cancers, but there are still no drugs that directly target it in the clinic. Recent studies utilizing dynamics information show promising results for selectively targeting mutant K-Ras. However, despite extensive characterization, the mechanisms by which K-Ras residue fluctuations transfer allosteric regulatory information remain unknown. Understanding the direction of information flow can provide new mechanistic insights for K-Ras targeting. Here, we present a novel approach –conditional time-delayed correlations (CTC) – using the motions of all residue pairs of a protein to predict directionality in the allosteric regulation of the protein fluctuations. Analyzing nucleotide-dependent intrinsic K-Ras motions with the new approach yields predictions that agree with the literature, showing that GTP-binding stabilizes K-Ras motions and leads to residue correlations with relatively long characteristic decay times. Furthermore, our study is the first to identify driver-follower relationships in correlated motions of K-Ras residue pairs, revealing the direction of information flow during allosteric modulation of its nucleotide-dependent intrinsic activity: active K-Ras Switch-II region motions drive Switch-I region motions, while α-helix-3L7 motions control both. Our results provide novel insights for strategies that directly target mutant K-Ras.

Drugging Ras GTPase: a comprehensive mechanistic and signaling structural view

Ras proteins are small GTPases, cycling between inactive GDP-bound and active GTP-bound states. Through these switches they regulate signaling that controls cell growth and proliferation. Activating Ras mutations are associated with approximately 30% of human cancers, which are frequently resistant to standard therapies. Over the past few years, structural biology and in silico drug design, coupled with improved screening technology, led to a handful of promising inhibitors, raising the possibility of drugging Ras proteins. At the same time, the invariable emergence of drug resistance argues for the critical importance of additionally honing in on signaling pathways which are likely to be involved. Here we overview current advances in Ras structural knowledge, including the conformational dynamic of full-length Ras in solution and at the membrane, therapeutic inhibition of Ras activity by targeting its active site, allosteric sites, and Ras-effector protein-protein interfaces, Ras dimers, the K-Ras4B/calmodulin/PI3Kα trimer, and targeting Ras with siRNA. To mitigate drug resistance, we propose signaling pathways that can be co-targeted along with Ras and explain why. These include pathways leading to the expression (or activation) of YAP1 and c-Myc. We postulate that these and Ras signaling pathways, MAPK/ERK and PI3K/Akt/mTOR, act independently and in corresponding ways in cell cycle control. The structural data are instrumental in the discovery and development of Ras inhibitors for treating RAS-driven cancers. Together with the signaling blueprints through which drug resistance can evolve, this review provides a comprehensive and innovative master plan for tackling mutant Ras proteins.

Conformational dynamics of cancer-associated MyD88-TIR domain mutant L252P (L265P) allosterically tilts the landscape toward homo-dimerization

MyD88 is an essential adaptor protein, which mediates the signaling of the toll-like and interleukin-1 receptors' superfamily. The MyD88 L252P (L265P) mutation has been identified in diffuse large B-cell lymphoma. The identification of this mutation has been a major advance in the diagnosis of patients with aldenstrom macroglobulinemia and related lymphoid neoplasms. Here we used computational methods to characterize the conformational effects of the mutation. Our molecular dynamics simulations revealed that the mutation allosterically quenched the global conformational dynamics of the toll/IL-1R (TIR) domain, and readjusted its salt bridges and dynamic community network. Specifically, the mutation changed the orientation and reduced the fluctuation of α-helix 3, possibly through eliminating/weakening ~8 salt bridges and enhancing the salt bridge D225-K258. Using the energy landscape of the TIR domains of MyD88, we identified two dynamic conformational basins, which correspond to the binding sites used in homo- and hetero-oligomerization, respectively. Our results indicate that the mutation stabilizes the core of the homo-dimer interface of the MyD88-TIR domain, and increases the population of homo-dimer-compatible conformational states in MyD88 family proteins. However, the dampened motion restricts its ability to heterodimerize with other TIR domains, thereby curtailing physiological signaling. In conclusion, the L252P both shifts the landscape toward homo-dimerization and restrains the dynamics of the MyD88-TIR domain, which disfavors its hetero-dimerization with other TIR domains. We further put these observations within the framework of MyD88-mediated cell signaling.

Computing energy landscape maps and structural excursions of proteins

BACKGROUND

Structural excursions of a protein at equilibrium are key to biomolecular recognition and function modulation. Protein modeling research is driven by the need to aid wet laboratories in characterizing equilibrium protein dynamics. In principle, structural excursions of a protein can be directly observed via simulation of its dynamics, but the disparate temporal scales involved in such excursions make this approach computationally impractical. On the other hand, an informative representation of the structure space available to a protein at equilibrium can be obtained efficiently via stochastic optimization, but this approach does not directly yield information on equilibrium dynamics.

METHODS

We present here a novel methodology that first builds a multi-dimensional map of the energy landscape that underlies the structure space of a given protein and then queries the computed map for energetically-feasible excursions between structures of interest. An evolutionary algorithm builds such maps with a practical computational budget. Graphical techniques analyze a computed multi-dimensional map and expose interesting features of an energy landscape, such as basins and barriers. A path searching algorithm then queries a nearest-neighbor graph representation of a computed map for energetically-feasible basin-to-basin excursions.

RESULTS

Evaluation is conducted on intrinsically-dynamic proteins of importance in human biology and disease. Visual statistical analysis of the maps of energy landscapes computed by the proposed methodology reveals features already captured in the wet laboratory, as well as new features indicative of interesting, unknown thermodynamically-stable and semi-stable regions of the equilibrium structure space. Comparison of maps and structural excursions computed by the proposed methodology on sequence variants of a protein sheds light on the role of equilibrium structure and dynamics in the sequence-function relationship.

CONCLUSIONS

Applications show that the proposed methodology is effective at locating basins in complex energy landscapes and computing basin-basin excursions of a protein with a practical computational budget. While the actual temporal scales spanned by a structural excursion cannot be directly obtained due to the foregoing of simulation of dynamics, hypotheses can be formulated regarding the impact of sequence mutations on protein function. These hypotheses are valuable in instigating further research in wet laboratories.

Principles and Overview of Sampling Methods for Modeling Macromolecular Structure and Dynamics

Investigation of macromolecular structure and dynamics is fundamental to understanding how macromolecules carry out their functions in the cell. Significant advances have been made toward this end in silico, with a growing number of computational methods proposed yearly to study and simulate various aspects of macromolecular structure and dynamics. This review aims to provide an overview of recent advances, focusing primarily on methods proposed for exploring the structure space of macromolecules in isolation and in assemblies for the purpose of characterizing equilibrium structure and dynamics. In addition to surveying recent applications that showcase current capabilities of computational methods, this review highlights state-of-the-art algorithmic techniques proposed to overcome challenges posed in silico by the disparate spatial and time scales accessed by dynamic macromolecules. This review is not meant to be exhaustive, as such an endeavor is impossible, but rather aims to balance breadth and depth of strategies for modeling macromolecular structure and dynamics for a broad audience of novices and experts.

The Structural Basis of Oncogenic Mutations G12, G13 and Q61 in Small GTPase K-Ras4B

Ras mediates cell proliferation, survival and differentiation. Mutations in K-Ras4B are predominant at residues G12, G13 and Q61. Even though all impair GAP-assisted GTP → GDP hydrolysis, the mutation frequencies of K-Ras4B in human cancers vary. Here we aim to figure out their mechanisms and differential oncogenicity. In total, we performed 6.4 μs molecular dynamics simulations on the wild-type K-Ras4B (K-Ras4B(WT)-GTP/GDP) catalytic domain, the K-Ras4B(WT)-GTP-GAP complex, and the mutants (K-Ras4B(G12C/G12D/G12V)-GTP/GDP, K-Ras4B(G13D)-GTP/GDP, K-Ras4B(Q61H)-GTP/GDP) and their complexes with GAP. In addition, we simulated 'exchanged' nucleotide states. These comprehensive simulations reveal that in solution K-Ras4B(WT)-GTP exists in two, active and inactive, conformations. Oncogenic mutations differentially elicit an inactive-to-active conformational transition in K-Ras4B-GTP; in K-Ras4B(G12C/G12D)-GDP they expose the bound nucleotide which facilitates the GDP-to-GTP exchange. These mechanisms may help elucidate the differential mutational statistics in K-Ras4B-driven cancers. Exchanged nucleotide simulations reveal that the conformational transition is more accessible in the GTP-to-GDP than in the GDP-to-GTP exchange. Importantly, GAP not only donates its R789 arginine finger, but stabilizes the catalytically-competent conformation and pre-organizes catalytic residue Q61; mutations disturb the R789/Q61 organization, impairing GAP-mediated GTP hydrolysis. Together, our simulations help provide a mechanistic explanation of key mutational events in one of the most oncogenic proteins in cancer.

Ras Conformational Ensembles, Allostery, and Signaling

Ras proteins are classical members of small GTPases that function as molecular switches by alternating between inactive GDP-bound and active GTP-bound states. Ras activation is regulated by guanine nucleotide exchange factors that catalyze the exchange of GDP by GTP, and inactivation is terminated by GTPase-activating proteins that accelerate the intrinsic GTP hydrolysis rate by orders of magnitude. In this review, we focus on data that have accumulated over the past few years pertaining to the conformational ensembles and the allosteric regulation of Ras proteins and their interpretation from our conformational landscape standpoint. The Ras ensemble embodies all states, including the ligand-bound conformations, the activated (or inactivated) allosteric modulated states, post-translationally modified states, mutational states, transition states, and nonfunctional states serving as a reservoir for emerging functions. The ensemble is shifted by distinct mutational events, cofactors, post-translational modifications, and different membrane compositions. A better understanding of Ras biology can contribute to therapeutic strategies.

A stochastic roadmap method to model protein structural transitions

Evidence is emerging that the role of protein structure in disease needs to be rethought. Sequence mutations in proteins are often found to affect the rate at which a protein switches between structures. Modeling structural transitions in wildtype and variant proteins is central to understanding the molecular basis of disease. This paper investigates an efficient algorithmic realization of the stochastic roadmap simulation framework to model structural transitions in wildtype and variants of proteins implicated in human disorders. Our results indicate that the algorithm is able to extract useful information on the impact of mutations on protein structure and function.