Publications by authors named "Anil Korkut"

19 Publications

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Combining Neratinib with CDK4/6, mTOR, and MEK Inhibitors in Models of HER2-positive Cancer.

Clin Cancer Res 2021 Jan 7. Epub 2021 Jan 7.

Department of Investigational Cancer Therapeutics, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Purpose: Neratinib is an irreversible, pan-HER tyrosine kinase inhibitor that is FDA approved for HER2-overexpressing/amplified (HER2) breast cancer. In this preclinical study, we explored the efficacy of neratinib in combination with inhibitors of downstream signaling in HER2 cancers and .

Experimental Design: Cell viability, colony formation assays, and Western blotting were used to determine the effect of neratinib . efficacy was assessed with patient-derived xenografts (PDX): two breast, two colorectal, and one esophageal cancer (with mutations). Four PDXs were derived from patients who received previous HER2-targeted therapy. Proteomics were assessed through reverse phase protein arrays and network-level adaptive responses were assessed through Target Score algorithm.

Results: In HER2 breast cancer cells, neratinib was synergistic with multiple agents, including mTOR inhibitors everolimus and sapanisertib, MEK inhibitor trametinib, CDK4/6 inhibitor palbociclib, and PI3Kα inhibitor alpelisib. We tested efficacy of neratinib with everolimus, trametinib, or palbociclib in five HER2 PDXs. Neratinib combined with everolimus or trametinib led to a 100% increase in median event-free survival (EFS; tumor doubling time) in 25% (1/4) and 60% (3/5) of models, respectively, while neratinib with palbociclib increased EFS in all five models. Network analysis of adaptive responses demonstrated upregulation of EGFR and HER2 signaling in response to CDK4/6, mTOR, and MEK inhibition, possibly providing an explanation for the observed synergies with neratinib.

Conclusions: Taken together, our results provide strong preclinical evidence for combining neratinib with CDK4/6, mTOR, and MEK inhibitors for the treatment of HER2 cancer.
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http://dx.doi.org/10.1158/1078-0432.CCR-20-3017DOI Listing
January 2021

CellBox: Interpretable Machine Learning for Perturbation Biology with Application to the Design of Cancer Combination Therapy.

Cell Syst 2021 Feb 28;12(2):128-140.e4. Epub 2020 Dec 28.

Department of Cell Biology, Harvard Medical School, Boston, MA, USA; cBio Center, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA. Electronic address:

Systematic perturbation of cells followed by comprehensive measurements of molecular and phenotypic responses provides informative data resources for constructing computational models of cell biology. Models that generalize well beyond training data can be used to identify combinatorial perturbations of potential therapeutic interest. Major challenges for machine learning on large biological datasets are to find global optima in a complex multidimensional space and mechanistically interpret the solutions. To address these challenges, we introduce a hybrid approach that combines explicit mathematical models of cell dynamics with a machine-learning framework, implemented in TensorFlow. We tested the modeling framework on a perturbation-response dataset of a melanoma cell line after drug treatments. The models can be efficiently trained to describe cellular behavior accurately. Even though completely data driven and independent of prior knowledge, the resulting de novo network models recapitulate some known interactions. The approach is readily applicable to various kinetic models of cell biology. A record of this paper's Transparent Peer Review process is included in the Supplemental Information.
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http://dx.doi.org/10.1016/j.cels.2020.11.013DOI Listing
February 2021

Large-Scale Characterization of Drug Responses of Clinically Relevant Proteins in Cancer Cell Lines.

Cancer Cell 2020 Dec 5;38(6):829-843.e4. Epub 2020 Nov 5.

Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Department of Systems Biology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; Graduate Program in Quantitative and Computational Biosciences, Baylor College of Medicine, Houston, TX 77030, USA. Electronic address:

Perturbation biology is a powerful approach to modeling quantitative cellular behaviors and understanding detailed disease mechanisms. However, large-scale protein response resources of cancer cell lines to perturbations are not available, resulting in a critical knowledge gap. Here we generated and compiled perturbed expression profiles of ∼210 clinically relevant proteins in >12,000 cancer cell line samples in response to ∼170 drug compounds using reverse-phase protein arrays. We show that integrating perturbed protein response signals provides mechanistic insights into drug resistance, increases the predictive power for drug sensitivity, and helps identify effective drug combinations. We build a systematic map of "protein-drug" connectivity and develop a user-friendly data portal for community use. Our study provides a rich resource to investigate the behaviors of cancer cells and the dependencies of treatment responses, thereby enabling a broad range of biomedical applications.
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http://dx.doi.org/10.1016/j.ccell.2020.10.008DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7738392PMC
December 2020

Perturbation biology links temporal protein changes to drug responses in a melanoma cell line.

PLoS Comput Biol 2020 07 15;16(7):e1007909. Epub 2020 Jul 15.

Department of Cell Biology, Harvard Medical School, Boston, MA 02115, U.S.A.

Cancer cells have genetic alterations that often directly affect intracellular protein signaling processes allowing them to bypass control mechanisms for cell death, growth and division. Cancer drugs targeting these alterations often work initially, but resistance is common. Combinations of targeted drugs may overcome or prevent resistance, but their selection requires context-specific knowledge of signaling pathways including complex interactions such as feedback loops and crosstalk. To infer quantitative pathway models, we collected a rich dataset on a melanoma cell line: Following perturbation with 54 drug combinations, we measured 124 (phospho-)protein levels and phenotypic response (cell growth, apoptosis) in a time series from 10 minutes to 67 hours. From these data, we trained time-resolved mathematical models that capture molecular interactions and the coupling of molecular levels to cellular phenotype, which in turn reveal the main direct or indirect molecular responses to each drug. Systematic model simulations identified novel combinations of drugs predicted to reduce the survival of melanoma cells, with partial experimental verification. This particular application of perturbation biology demonstrates the potential impact of combining time-resolved data with modeling for the discovery of new combinations of cancer drugs.
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http://dx.doi.org/10.1371/journal.pcbi.1007909DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7384681PMC
July 2020

Integrated Analysis of TP53 Gene and Pathway Alterations in The Cancer Genome Atlas.

Cell Rep 2019 07;28(5):1370-1384.e5

Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA.

The TP53 tumor suppressor gene is frequently mutated in human cancers. An analysis of five data platforms in 10,225 patient samples from 32 cancers reported by The Cancer Genome Atlas (TCGA) enables comprehensive assessment of p53 pathway involvement in these cancers. More than 91% of TP53-mutant cancers exhibit second allele loss by mutation, chromosomal deletion, or copy-neutral loss of heterozygosity. TP53 mutations are associated with enhanced chromosomal instability, including increased amplification of oncogenes and deep deletion of tumor suppressor genes. Tumors with TP53 mutations differ from their non-mutated counterparts in RNA, miRNA, and protein expression patterns, with mutant TP53 tumors displaying enhanced expression of cell cycle progression genes and proteins. A mutant TP53 RNA expression signature shows significant correlation with reduced survival in 11 cancer types. Thus, TP53 mutation has profound effects on tumor cell genomic structure, expression, and clinical outlook.
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http://dx.doi.org/10.1016/j.celrep.2019.07.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7546539PMC
July 2019

A Pan-Cancer Analysis Reveals High-Frequency Genetic Alterations in Mediators of Signaling by the TGF-β Superfamily.

Cell Syst 2018 10 26;7(4):422-437.e7. Epub 2018 Sep 26.

Department of Bioinformatics and Computational Biology, MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address:

We present an integromic analysis of gene alterations that modulate transforming growth factor β (TGF-β)-Smad-mediated signaling in 9,125 tumor samples across 33 cancer types in The Cancer Genome Atlas (TCGA). Focusing on genes that encode mediators and regulators of TGF-β signaling, we found at least one genomic alteration (mutation, homozygous deletion, or amplification) in 39% of samples, with highest frequencies in gastrointestinal cancers. We identified mutation hotspots in genes that encode TGF-β ligands (BMP5), receptors (TGFBR2, AVCR2A, and BMPR2), and Smads (SMAD2 and SMAD4). Alterations in the TGF-β superfamily correlated positively with expression of metastasis-associated genes and with decreased survival. Correlation analyses showed the contributions of mutation, amplification, deletion, DNA methylation, and miRNA expression to transcriptional activity of TGF-β signaling in each cancer type. This study provides a broad molecular perspective relevant for future functional and therapeutic studies of the diverse cancer pathways mediated by the TGF-β superfamily.
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http://dx.doi.org/10.1016/j.cels.2018.08.010DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6370347PMC
October 2018

A Comprehensive Pan-Cancer Molecular Study of Gynecologic and Breast Cancers.

Cancer Cell 2018 04 2;33(4):690-705.e9. Epub 2018 Apr 2.

Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA. Electronic address:

We analyzed molecular data on 2,579 tumors from The Cancer Genome Atlas (TCGA) of four gynecological types plus breast. Our aims were to identify shared and unique molecular features, clinically significant subtypes, and potential therapeutic targets. We found 61 somatic copy-number alterations (SCNAs) and 46 significantly mutated genes (SMGs). Eleven SCNAs and 11 SMGs had not been identified in previous TCGA studies of the individual tumor types. We found functionally significant estrogen receptor-regulated long non-coding RNAs (lncRNAs) and gene/lncRNA interaction networks. Pathway analysis identified subtypes with high leukocyte infiltration, raising potential implications for immunotherapy. Using 16 key molecular features, we identified five prognostic subtypes and developed a decision tree that classified patients into the subtypes based on just six features that are assessable in clinical laboratories.
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http://dx.doi.org/10.1016/j.ccell.2018.03.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5959730PMC
April 2018

The RNA-binding Protein MEX3B Mediates Resistance to Cancer Immunotherapy by Downregulating HLA-A Expression.

Clin Cancer Res 2018 07 1;24(14):3366-3376. Epub 2018 Mar 1.

Department of Melanoma Medical Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas.

Cancer immunotherapy has shown promising clinical outcomes in many patients. However, some patients still fail to respond, and new strategies are needed to overcome resistance. The purpose of this study was to identify novel genes and understand the mechanisms that confer resistance to cancer immunotherapy. To identify genes mediating resistance to T-cell killing, we performed an open reading frame (ORF) screen of a kinome library to study whether overexpression of a gene in patient-derived melanoma cells could inhibit their susceptibility to killing by autologous tumor-infiltrating lymphocytes (TIL). The RNA-binding protein MEX3B was identified as a top candidate that decreased the susceptibility of melanoma cells to killing by TILs. Further analyses of anti-PD-1-treated melanoma patient tumor samples suggested that higher expression is associated with resistance to PD-1 blockade. In addition, significantly decreased levels of IFNγ were secreted from TILs incubated with MEX3B-overexpressing tumor cells. Interestingly, this phenotype was rescued upon overexpression of exogenous HLA-A2. Consistent with this, we observed decreased HLA-A expression in MEX3B-overexpressing tumor cells. Finally, luciferase reporter assays and RNA-binding protein immunoprecipitation assays suggest that this is due to MEX3B binding to the 3' untranslated region (UTR) of to destabilize the mRNA. MEX3B mediates resistance to cancer immunotherapy by binding to the 3' UTR of to destabilize the mRNA and thus downregulate HLA-A expression on the surface of tumor cells, thereby making the tumor cells unable to be recognized and killed by T cells. .
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http://dx.doi.org/10.1158/1078-0432.CCR-17-2483DOI Listing
July 2018

Proteomics profiling identifies induction of caveolin-1 in chronic lymphocytic leukemia cells by bone marrow stromal cells.

Leuk Lymphoma 2018 06 3;59(6):1427-1438. Epub 2017 Oct 3.

a Department of Experimental Therapeutics , The University of Texas MD Anderson Cancer Center , Houston , TX , USA.

Chronic lymphocytic leukemia (CLL) is an indolent B-cell malignancy in which cells reside in bone marrow, lymph nodes, and peripheral blood, each of which provides a unique microenvironment. Although the levels of certain proteins are reported to induce, changes in the CLL cell proteome in the presence of bone marrow stromal cells have not been elucidated. Reverse-phase protein array analysis of CLL cells before and 24 h after stromal cell interaction revealed changed levels of proteins that regulate cell cycle, gene transcription, and protein translation. The most hit with respect to both the extent of change in expression level and statistical significance was caveolin-1, which was confirmed with immunoblotting. Caveolin-1 mRNA levels were also upregulated in CLL cells after stromal cell interaction. The induction of caveolin-1 levels was rapid and occurred as early as 1 h. Studies to determine the significance of upregulated caveolin-1 levels in CLL lymphocytes are warranted.
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http://dx.doi.org/10.1080/10428194.2017.1376747DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5882591PMC
June 2018

Changes in Bcl-2 Family Protein Profile During Idelalisib Therapy Mimic Those During Duvelisib Therapy in Chronic Lymphocytic Leukemia Lymphocytes.

JCO Precis Oncol 2017 28;1. Epub 2017 Jun 28.

, , , and , The University of Texas MD Anderson Cancer Center, Houston, TX; , , and , Dana-Farber Cancer Institute, Boston, MA.

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http://dx.doi.org/10.1200/PO.17.00025DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7446340PMC
June 2017

Pan-Cancer Analysis of Mutation Hotspots in Protein Domains.

Cell Syst 2015 Sep 23;1(3):197-209. Epub 2015 Sep 23.

Computational Biology Program, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA. Electronic address:

In cancer genomics, recurrence of mutations in independent tumor samples is a strong indicator of functional impact. However, rare functional mutations can escape detection by recurrence analysis owing to lack of statistical power. We enhance statistical power by extending the notion of recurrence of mutations from single genes to gene families that share homologous protein domains. Domain mutation analysis also sharpens the functional interpretation of the impact of mutations, as domains more succinctly embody function than entire genes. By mapping mutations in 22 different tumor types to equivalent positions in multiple sequence alignments of domains, we confirm well-known functional mutation hotspots, identify uncharacterized rare variants in one gene that are equivalent to well-characterized mutations in another gene, detect previously unknown mutation hotspots, and provide hypotheses about molecular mechanisms and downstream effects of domain mutations. With the rapid expansion of cancer genomics projects, protein domain hotspot analysis will likely provide many more leads linking mutations in proteins to the cancer phenotype.
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http://dx.doi.org/10.1016/j.cels.2015.08.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4982675PMC
September 2015

Perturbation biology nominates upstream-downstream drug combinations in RAF inhibitor resistant melanoma cells.

Elife 2015 Aug 18;4. Epub 2015 Aug 18.

Computational Biology Center, Memorial Sloan Kettering Cancer Center, New York, United States.

Resistance to targeted cancer therapies is an important clinical problem. The discovery of anti-resistance drug combinations is challenging as resistance can arise by diverse escape mechanisms. To address this challenge, we improved and applied the experimental-computational perturbation biology method. Using statistical inference, we build network models from high-throughput measurements of molecular and phenotypic responses to combinatorial targeted perturbations. The models are computationally executed to predict the effects of thousands of untested perturbations. In RAF-inhibitor resistant melanoma cells, we measured 143 proteomic/phenotypic entities under 89 perturbation conditions and predicted c-Myc as an effective therapeutic co-target with BRAF or MEK. Experiments using the BET bromodomain inhibitor JQ1 affecting the level of c-Myc protein and protein kinase inhibitors targeting the ERK pathway confirmed the prediction. In conclusion, we propose an anti-cancer strategy of co-targeting a specific upstream alteration and a general downstream point of vulnerability to prevent or overcome resistance to targeted drugs.
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http://dx.doi.org/10.7554/eLife.04640DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4539601PMC
August 2015

Spatial normalization of reverse phase protein array data.

PLoS One 2014 12;9(12):e97213. Epub 2014 Dec 12.

Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

Reverse phase protein arrays (RPPA) are an efficient, high-throughput, cost-effective method for the quantification of specific proteins in complex biological samples. The quality of RPPA data may be affected by various sources of error. One of these, spatial variation, is caused by uneven exposure of different parts of an RPPA slide to the reagents used in protein detection. We present a method for the determination and correction of systematic spatial variation in RPPA slides using positive control spots printed on each slide. The method uses a simple bi-linear interpolation technique to obtain a surface representing the spatial variation occurring across the dimensions of a slide. This surface is used to calculate correction factors that can normalize the relative protein concentrations of the samples on each slide. The adoption of the method results in increased agreement between technical and biological replicates of various tumor and cell-line derived samples. Further, in data from a study of the melanoma cell-line SKMEL-133, several slides that had previously been rejected because they had a coefficient of variation (CV) greater than 15%, are rescued by reduction of CV below this threshold in each case. The method is implemented in the R statistical programing language. It is compatible with MicroVigene and SuperCurve, packages commonly used in RPPA data analysis. The method is made available, along with suggestions for implementation, at http://bitbucket.org/rppa_preprocess/rppa_preprocess/src.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0097213PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4264691PMC
January 2016

Perturbation biology: inferring signaling networks in cellular systems.

PLoS Comput Biol 2013 19;9(12):e1003290. Epub 2013 Dec 19.

Computational Biology Program, Memorial Sloan-Kettering Cancer Center, New York, New York, United States of America.

We present a powerful experimental-computational technology for inferring network models that predict the response of cells to perturbations, and that may be useful in the design of combinatorial therapy against cancer. The experiments are systematic series of perturbations of cancer cell lines by targeted drugs, singly or in combination. The response to perturbation is quantified in terms of relative changes in the measured levels of proteins, phospho-proteins and cellular phenotypes such as viability. Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. The prohibitively large solution space of all possible network models is explored efficiently using a probabilistic algorithm, Belief Propagation (BP), which is three orders of magnitude faster than standard Monte Carlo methods. Explicit executable models are derived for a set of perturbation experiments in SKMEL-133 melanoma cell lines, which are resistant to the therapeutically important inhibitor of RAF kinase. The resulting network models reproduce and extend known pathway biology. They empower potential discoveries of new molecular interactions and predict efficacious novel drug perturbations, such as the inhibition of PLK1, which is verified experimentally. This technology is suitable for application to larger systems in diverse areas of molecular biology.
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http://dx.doi.org/10.1371/journal.pcbi.1003290DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3868523PMC
August 2014

Drug synergy screen and network modeling in dedifferentiated liposarcoma identifies CDK4 and IGF1R as synergistic drug targets.

Sci Signal 2013 Sep 24;6(294):ra85. Epub 2013 Sep 24.

1Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, NY 10065, USA.

Dedifferentiated liposarcoma (DDLS) is a rare but aggressive cancer with high recurrence and low response rates to targeted therapies. Increasing treatment efficacy may require combinations of targeted agents that counteract the effects of multiple abnormalities. To identify a possible multicomponent therapy, we performed a combinatorial drug screen in a DDLS-derived cell line and identified cyclin-dependent kinase 4 (CDK4) and insulin-like growth factor 1 receptor (IGF1R) as synergistic drug targets. We measured the phosphorylation of multiple proteins and cell viability in response to systematic drug combinations and derived computational models of the signaling network. These models predict that the observed synergy in reducing cell viability with CDK4 and IGF1R inhibitors depends on the activity of the AKT pathway. Experiments confirmed that combined inhibition of CDK4 and IGF1R cooperatively suppresses the activation of proteins within the AKT pathway. Consistent with these findings, synergistic reductions in cell viability were also found when combining CDK4 inhibition with inhibition of either AKT or epidermal growth factor receptor (EGFR), another receptor similar to IGF1R that activates AKT. Thus, network models derived from context-specific proteomic measurements of systematically perturbed cancer cells may reveal cancer-specific signaling mechanisms and aid in the design of effective combination therapies.
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http://dx.doi.org/10.1126/scisignal.2004014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4000046PMC
September 2013

Structural plasticity and conformational transitions of HIV envelope glycoprotein gp120.

PLoS One 2012 27;7(12):e52170. Epub 2012 Dec 27.

Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY, USA.

HIV envelope glycoproteins undergo large-scale conformational changes as they interact with cellular receptors to cause the fusion of viral and cellular membranes that permits viral entry to infect targeted cells. Conformational dynamics in HIV gp120 are also important in masking conserved receptor epitopes from being detected for effective neutralization by the human immune system. Crystal structures of HIV gp120 and its complexes with receptors and antibody fragments provide high-resolution pictures of selected conformational states accessible to gp120. Here we describe systematic computational analyses of HIV gp120 plasticity in such complexes with CD4 binding fragments, CD4 mimetic proteins, and various antibody fragments. We used three computational approaches: an isotropic elastic network analysis of conformational plasticity, a full atomic normal mode analysis, and simulation of conformational transitions with our coarse-grained virtual atom molecular mechanics (VAMM) potential function. We observe collective sub-domain motions about hinge points that coordinate those motions, correlated local fluctuations at the interfacial cavity formed when gp120 binds to CD4, and concerted changes in structural elements that form at the CD4 interface during large-scale conformational transitions to the CD4-bound state from the deformed states of gp120 in certain antibody complexes.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0052170PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531394PMC
July 2013

A force field for virtual atom molecular mechanics of proteins.

Proc Natl Acad Sci U S A 2009 Sep 28;106(37):15667-72. Epub 2009 Aug 28.

Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.

Activities of many biological macromolecules involve large conformational transitions for which crystallography can specify atomic details of alternative end states, but the course of transitions is often beyond the reach of computations based on full-atomic potential functions. We have developed a coarse-grained force field for molecular mechanics calculations based on the virtual interactions of C alpha atoms in protein molecules. This force field is parameterized based on the statistical distribution of the energy terms extracted from crystallographic data, and it is formulated to capture features dependent on secondary structure and on residue-specific contact information. The resulting force field is applied to energy minimization and normal mode analysis of several proteins. We find robust convergence in minimizations to low energies and energy gradients with low degrees of structural distortion, and atomic fluctuations calculated from the normal mode analyses correlate well with the experimental B-factors obtained from high-resolution crystal structures. These findings suggest that the virtual atom force field is a suitable tool for various molecular mechanics applications on large macromolecular systems undergoing large conformational changes.
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http://dx.doi.org/10.1073/pnas.0907674106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2734882PMC
September 2009

Computation of conformational transitions in proteins by virtual atom molecular mechanics as validated in application to adenylate kinase.

Proc Natl Acad Sci U S A 2009 Sep 25;106(37):15673-8. Epub 2009 Aug 25.

Department of Biochemistry and Molecular Biophysics, Columbia University, New York, NY 10032, USA.

Many proteins function through conformational transitions between structurally disparate states, and there is a need to explore transition pathways between experimentally accessible states by computation. The sizes of systems of interest and the scale of conformational changes are often beyond the scope of full atomic models, but appropriate coarse-grained approaches can capture significant features. We have designed a comprehensive knowledge-based potential function based on a C alpha representation for proteins that we call the virtual atom molecular mechanics (VAMM) force field. Here, we describe an algorithm for using the VAMM potential to describe conformational transitions, and we validate this algorithm in application to a transition between open and closed states of adenylate kinase (ADK). The VAMM algorithm computes normal modes for each state and iteratively moves each structure toward the other through a series of intermediates. The move from each side at each step is taken along that normal mode showing greatest engagement with the other state. The process continues to convergence of terminal intermediates to within a defined limit--here, a root-mean-square deviation of 1 A. Validations show that the VAMM algorithm is highly effective, and the transition pathways examined for ADK are compatible with other structural and biophysical information. We expect that the VAMM algorithm can address many biological systems.
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http://dx.doi.org/10.1073/pnas.0907684106DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2731797PMC
September 2009