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Noncovalent Lasso Entanglements are Common in Experimentally Derived Intrinsically Disordered Protein Ensembles and Strongly Influenced by Protein Length and Charge

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Journal of Physical Chemistry B 2025年第19期129卷 4682-4691页

作者： Vu, Quyen V. Sitarik, Ian Li, Mai Suan O’Brien, Edward P. Department of Chemistry Pennsylvania State University University ParkPA16802 United States Institute of Physics Polish Academy of Sciences Al. Lotnikow 32/46 Warsaw02-668 Poland Institute for Computational Sciences and Technology Quang Trung Software City Tan Chanh Hiep Ward District 12 Ho Chi Minh City510000 Viet Nam Institute for Computational and Data Sciences Pennsylvania State University University ParkPA16802 United States Bioinformatics and Genomics Graduate Program The Huck Institutes of the Life Sciences Pennsylvania State University University ParkPA16802 United States

Noncovalent lasso entanglements are conformations in which a protein backbone segment forms a loop closed by noncovalent interactions and that loop is threaded one or more times by either the N- or C-terminal segment of the backbone or both. While these entanglements are common in globular proteins, their presence in intrinsically disordered proteins or regions (IDPs/IDRs) remains largely unexplored. Here, we examine whether IDPs/IDRs in their monomeric form populate these conformations and how sequence length and charge composition influence entanglement prevalence. Using experimentally derived IDP/IDR ensembles from the Protein Ensemble Database, we find that 48% (199 of 416) of its entries contain subpopulations with entangled conformations, with 25% of entries having conformational ensembles in which 50% or more are entangled. This includes IDPs such as nuclear pore complex protein Nup153, nonstructural protein V of Hendra virus, and Eukaryotic initiation factor 4F subunit p150. Using molecular simulations, we find that (i) entanglements are most prevalent in weak polyampholytes and polyelectrolytes, and strong polyampholytes but rare in strong polyelectrolytes;(ii) entanglement populations increase with IDP length;(iii) entanglement probability positively correlates with chain compaction;and (iv) most IDPs/IDRs in the human proteome exhibit entangled conformations. A GO enrichment analysis reveals that the entanglement probability correlates with IDP/IDR function and subcellular localization. Thus, these findings indicate that noncovalent lasso entanglements are a widespread structural feature of IDPs/IDRs and have the potential to be biologically relevant. © 2025 American Chemical Society.

关键词： Quantum entanglement

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Insights into the binding interactions of Macitentan to bovine serum Albumin: A Spectroscopic, electrochemical and computational study

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Journal of Molecular Liquids 2025年 429卷

作者： Sadak, Selenay Dindar, Çiğdem Kanbeş Kabir, Md.Zahirul Rehman, Fazal Mohamad, Saharuddin B. Yildirim, Sercan Uslu, Bengi Ankara University Faculty of Pharmacy Department of Analytical Chemistry Ankara06560 Turkey Ankara University The Graduate School of Health Sciences Ankara06110 Turkey Biochemistry Program Institute of Biological Sciences Faculty of Science University Malaya Kuala Lumpur Malaysia Faculty of Science Bioinformatics Programme Institute of Biological Sciences Malaysia Centre of Research for Computational Sciences and Informatics for Biology Bioindustry Environment Agriculture and Healthcare University of Malaya Kuala Lumpur Malaysia Karadeniz Technical University Faculty of Pharmacy Department of Analytical Chemistry Trabzon Turkey

Macitentan (MACI) is an endothelial receptor antagonist used to treat pulmonary arterial hypertension (PAH). The pharmacokinetics and therapeutic efficacy of MACI are significantly influenced by its interaction with plasma proteins, especially bovine serum albumin (BSA). In this study, spectroscopic (fluorescence and UV–visible absorption spectroscopy), electrochemical, and computational techniques were applied to explore the binding properties and interaction mechanisms between MACI and BSA. The results indicated that the MACI quenched BSA fluorescence through a static mechanism. The interaction possessed a moderate binding affinity with Ka values ranging from 1.27 × 104 to 7.25 × 104 M−1 at different temperatures. Hydrophobic forces and hydrogen bonds were identified as critical factors in stabilizing the complex based on thermodynamic parameters. The local environment of tryptophan residues in BSA was found to be altered following the addition of MACI, as revealed by synchronous and three-dimensional fluorescence spectra. The spectroscopic studies were supported by electrochemical analyses, which indicated the formation of an electro-inactive complex between MACI and BSA. From the molecular docking experiments, the most likely binding site for MACI was found to be site I of BSA. Several hydrogen bonds and hydrophobic contacts were observed during the interaction of MACI with site I of BSA. These findings provided detailed insight to understand the binding properties of MACI to BSA, which may be beneficial for medical and pharmaceutical applications in the future. © 2025 Elsevier B.V.

关键词： Hydrogen bonds

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Unbiased Multiplex Antigen Screening of Cerebrospinal Fluid Detects Microbial and Autoantigenic Epitopes Associated with Multiple Sclerosis

SSRN

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SSRN 2025年

作者： Barton, Nathaniel J. Tran, Khanh Olson, Meagan N. Deshpande, Mugdha Radu, Irina Francis, Nimmy Kurban, Mariana Martin-Puig, Aurelie Wilner, Molly Orszulak, Adrian R. Chigas, Samantha M. Sundstrom, Jonathan Dawes, Pepper Murray, Liam F. Baiby, Marcus Ionete, Carolina Lim, Elaine T. Hemond, Christopher C. Chan, Yingleong Department of Neurology University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Medicine Division of Innate Immunity University of Massachusetts Chan Medical School WorcesterMA01605 United States NeuroNexus Institute University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Genomics and Computational Biology University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Molecular Cell and Cancer Biology University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Biochemistry & Molecular Biotechnology Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States Department of Medicine Division of Cardiovascular Medicine University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Immunology and Microbiology Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States Graduate Program in Neuroscience Morningside Graduate School of Biomedical Sciences University of Massachusetts Chan Medical School WorcesterMA01605 United States

To comprehensively investigate the intrathecal antibody profile of multiple sclerosis (MS), we examined the cerebrospinal fluid of 195 patients (92 MS and 103 non-MS) for antibodies using a multiplex unbiased bacteria peptide library. We first tested against Epstein-Barr nuclear antigen 1 (EBNA1) for epitope sites enriched in MS and found a significant enrichment at position 407-419. We then used the data to perform a high-throughput screen against a library of 129 viruses known to infect humans. We discovered several additional epitopes from viruses such as Hantaan virus, Human Herpesvirus 6A and Human respiratory syncytial virus B associated with MS. Besides viral epitopes, we also screened for potential autoantigens of the central nervous system (CNS). We discovered several autoantigenic epitopes in proteins such as ADRB3, HTR3A and MPO that were significantly enriched for MS. Because of previous associations of Toxoplasma gondii infection with MS, we also performed a Toxoplasma gondii specific analysis and discovered additional epitopes enriched for MS. We further assessed epitope-epitope correlations within the patient samples and found distinct patterns of association between these microbial and autoantigenic epitopes. Finally, we performed machine-learning to determine if these epitopes are predictive for MS and found that the model incorporating all the epitopes could most effectively discriminate between MS and non-MS (ROC-AUC score = 0.91). Our results demonstrate the effectiveness of multiplex unbiased screens to detect the identity of potentially cross-reactive antibodies targeting MS CNS epitopes and they can also be used as effective biomarkers for MS. © 2025, The Authors. All rights reserved.

关键词： Epitopes

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Survey and Improvement Strategies for Gene Prioritization with Large Language Models

arXiv

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arXiv 2025年

作者： Neeley, Matthew B. Qi, Guantong Wang, Guanchu Tang, Ruixiang Mao, Dongxue Liu, Chaozhong Pasupuleti, Sasidhar Yuan, Bo Xia, Fan Liu, Pengfei Liu, Zhandong Hu, Xia Jan and Dan Duncan Neurological Research Institute Texas Children’s Hospital Houston United States Graduate School of Biomedical Sciences Program in Quantitative and Computational Biosciences Baylor College of Medicine Houston United States Graduate School of Biomedical Sciences Program in Genetics and Genomics Baylor College of Medicine Houston United States Department of Computer Science Rice University Houston United States Department of Computer Science Rutgers University NJ United States Department of Pediatrics Baylor College of Medicine Houston United States Department of Molecular and Human Genetics Baylor College of Medicine Houston United States Human Genome Sequencing Center Baylor College of Medicine Houston United States Baylor Genetics Houston United States

Background Rare diseases can be difficult to diagnose due to limited patient data and broad genetic diversity. Despite the advances in variant prioritization tools, many rare disease cases remain undiagnosed. While large language models (LLMs) have performed well in medical exams, their accuracy in diagnosing rare genetic diseases has not yet been evaluated. Methods To identify causal genes for genetic diseases, we benchmarked various LLMs for gene prioritization. We employed multi-agent and Human Phenotype Ontology (HPO) classification approaches to identify patient case groups based on phenotypes categorizing them into different levels of solvability. To address LLM limitations in ranking large gene sets, we used a divide-and-conquer strategy to break down the ranking task into smaller subsets. Mini-batching inputs and limiting the number of generated tokens improved efficiency. Results In its vanilla form, GPT-4 consistently outperformed the other LLMs with an accuracy around 30%. Multi-agent and HPO classification approaches aided us in distinguishing between confidently-solved and challenging cases. In addition, we observed bias towards well-studied genes and input order sensitivity which are drawbacks of LLMs in disease-causal gene prioritization. Our divide-and-conquer strategy enhanced accuracy by overcoming positional and gene frequency biases in literature. This framework, based on benchmarking insights and novel techniques, significantly optimizes the overall process when identifying disease-causal genes for genetic diseases when compared to our baseline evaluation, and thereby better enables the development of targeted diagnostic and therapeutic interventions. Conclusions Using HPO classification and our novel multi-agent techniques, and our LLM divide-and-conquer strategy (1) highlighted the importance of accounting for differences in patient case solvability and (2) yielded improved performance in identifying causal genes for rare diseases when compare

关键词： Gene Ontology

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Biomedical data and AI

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Science China Life sciences 2025年第5期68卷 1536-1540页

作者： Hao Xu Shibo Zhou Zefeng Zhu Vincenzo Vitelli Liangyi Chen Ziwei Dai Ning Yang Luhua Lai Shengyong Yang Sergey Ovchinnikov Zhuoran Qiao Sirui Liu Chen Song Jianfeng Pei Han Wen Jianfeng Feng Yaoyao Zhang Zhengwei Xie Yang-Yu Liu Zhiyuan Li Fulai Jin Hao Li Mohammad Lotfollahi Xuegong Zhang Ge Yang Shihua Zhang Ge Gao Pulin Li Qi Liu Jing-Dong Jackie Han Peking-Tsinghua Center for Life Sciences (CLS) Academy for Advanced Interdisciplinary StudiesPeking University Center for Quantitative Biology (CQB) Academy for Advanced Interdisciplinary StudiesPeking University Peking University-Tsinghua University-National Institute of Biological Sciences Joint Graduate Program Academy for Advanced Interdisciplinary StudiesPeking University Department of Physics University of Chicago School of Life Sciences Southern University of Science and Technology Peking University Chengdu Academy for Advanced Interdisciplinary Biotechnologies College of Chemistry and Molecular Engineering Peking University Department of Biotherapy Cancer Center and State Key Laboratory of BiotherapyWest China HospitalSichuan University Department of Biology Massachusetts Institute of Technology Lambic Therapeutics Inc. Changping Laboratory Al for Science Institute Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan University Department of Obstetrics and Gynecology West China Second University HospitalSichuan University Peking University International Cancer Institute and Peking University-Yunnan Baiyao International Medical Institute and State Key Laboratory of Natural and Biomimetic Drugs Department of Molecular and Cellular PharmacologySchool of Pharmaceutical SciencesPeking University Health Science CenterPeking University Channing Division of Network Medicine Department of MedicineBrigham and Women's Hospital and Harvard Medical School Center for Artificial Intelligence and Modeling the Carl R.Woese Institute for Genomic BiologyUniversity of Illinois Urbana-Champaign Department of Genetics and Genome Sciences School of Medicine and Department of Computer and Data Sciences and Department of Population and Quantitative Health SciencesCase Western Reserve University Department of Biochemistry and Biophysics University of California Sanger Institute Department of Automation Tsinghua University State Key Laboratory of Multimodal Artificial Intelligence Systems I

The development of artificial intelligence(AI) and the mining of biomedical data complement each other. From the direct use of computer vision results to analyze medical images for disease screening, to now integrating biological knowledge into models and even accelerating the development of new AI based on biological discoveries, the boundaries of both are constantly expanding, and their connections are becoming closer.

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Drivetrain simulation using variational autoencoders

arXiv

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arXiv 2025年

作者： Sharma, Pallavi Urrea-Quintero, Jorge-Humberto Bogdan, Bogdan Ciotec, Adrian-Dumitru Vasilie, Laura Wessels, Henning Skull, Matteo Porsche Engineering Etzelstraße 1 Bietigheim-Bissingen74321 Germany Graduate Program Computational Sciences in Engineering Technische Universität Braunschweig Braunschweig Germany Fraunhofer Institute for Energy Economics and Energy System Technology Joseph-Beuys-Straße 8 Kassel34117 Germany Institute of Applied Mechanics Division Data-Driven Modeling of Mechanical Systems Technische Universität Braunschweig Pockelsstraße 3 Braunschweig38106 Germany

This work proposes variational autoencoders (VAEs) to predict a vehicle’s jerk from a given torque demand, addressing the limitations of sparse real-world datasets. Specifically, we implement unconditional and conditional VAEs to generate jerk signals that integrate features from different drivetrain scenarios. The VAEs are trained on experimental data collected from two variants of a fully electric SUV, which differ in maximum torque delivery and drivetrain configuration. New meaningful jerk signals are generated within an engineering context through the interpretation of the VAE’s latent space. A performance comparison with baseline physics-based and hybrid models confirms the effectiveness of the VAEs. We show that VAEs bypass the need for exhaustive manual system parametrization while maintaining physical plausibility by conditioning data generation on specific inputs. © 2025, CC BY.

关键词： Signal encoding

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Identification of novel TMEM16A blockers through integrated virtual screening, molecular dynamics, and experimental studies

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Scientific Reports 2025年第1期15卷 1-11页

作者： Sangkhawasi, Mattanun Pitaktrakul, Wichuda Khumjiang, Rungtiwa Shigeta, Yasuteru Muanprasat, Chatchai Hengphasatporn, Kowit Rungrotmongkol, Thanyada Center of Excellence in Biocatalyst and Sustainable Biotechnology Department of Biochemistry Faculty of Science Chulalongkorn University Bangkok 10330 Thailand Chakri Naruebodindra Medical Institute Faculty of Medicine Ramathibodi Hospital Mahidol University Bang Pla Bang Pli Samut Prakan 10540 Thailand Center for Computational Sciences University of Tsukuba 1-1-1 Tennodai Ibaraki Tsukuba 305-8577 Japan Program in Bioinformatics and Computational Biology Graduate School Chulalongkorn University Bangkok 10330 Thailand Division of Biochemistry Department of Preclinical Science Faculty of Medicine Thammasat University Pathumthani 12120 Thailand

The calcium-activated chloride channel TMEM16A is a promising drug target for treating hypertension, secretory diarrheas, and various cancers, including head and neck cancer. Despite its potential, no FDA-approved drugs have provided the structural basis for directly inhibiting TMEM16A. This study aims to identify a novel pore-blocker of TMEM16A by integrating virtual screening, molecular dynamics simulations, and in vitro studies. Using the calcium-bound structure of TMEM16A with and without the pore-blocker 1PBC, we performed virtual screening on nearly 90,000 compounds from the ChemDiv database. Approximately 67% of these compounds demonstrated better binding affinity than 1PBC. Among the top 20 compounds selected for short-circuit current assays using human lung adenocarcinoma cells (Calu-3), compounds N066-0059, N066-0060, and N066-0067 inhibited TMEM16A activity with IC₅₀ values of 0.24 µM, 0.41 µM, and 0.48 µM, respectively, which was lower than that of a positive control Ani9 (9 µM). Due to its highest potency in electrophysiological assays, N066-0059 was subjected to mechanistic studies. Molecular dynamics simulations elucidated its binding stability and strength, showing superior performance to 1PBC over 500 ns with 3 replicates. This study advances TMEM16A-targeted drug development, offering new insights for anticancer therapies. © The Author(s) 2025.

关键词： And electrophysiological assays Cytotoxicity Molecular dynamics Pore-blocker inhibitor TMEM16A Virtual screening

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Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge 1st

Overview of the Head and Neck Tumor Segmentation for Magneti...

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1st Challenge on Head and Neck Tumor Segmentation for MRI-Guided Applications, HNTS-MRG 2024, Held in Conjunction with 27th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2024

作者： Wahid, Kareem A. Dede, Cem El-Habashy, Dina M. Kamel, Serageldin Rooney, Michael K. Khamis, Yomna Abdelaal, Moamen R. A. Ahmed, Sara Corrigan, Kelsey L. Chang, Enoch Dudzinski, Stephanie O. Salzillo, Travis C. McDonald, Brigid A. Mulder, Samuel L. McCullum, Lucas Alakayleh, Qusai Sjogreen, Carlos He, Renjie Mohamed, Abdallah S. R. Lai, Stephen Y. Christodouleas, John P. Schaefer, Andrew J. Naser, Mohamed A. Fuller, Clifton D. Department of Radiation Oncology The University of Texas MD Anderson Cancer HoustonTX United States Department of Imaging Physics The University of Texas MD Anderson Cancer HoustonTX United States Transitional Year Program Corewell Health Wiliam Beaumont Royal OakMI United States Department of Radiation Oncology University of Maryland School of Medicine BaltimoreMD United States Department of Clinical Oncology and Nuclear Medicine Faculty of Medicine Alexandria University Alexandria Egypt UT MD Anderson Cancer CenterUTHealth Houston Graduate School of Biomedical Sciences Houston United States Department of Radiation Oncology Baylor College of Medicine HoustonTX United States Department of Head and Neck Surgery The University of Texas MD Anderson Cancer HoustonTX United States Elekta AtlantaGA United States Department of Computational Applied Mathematics and Operations Research Rice University HoustonTX United States

ISBN: (纸本)9783031832734

Magnetic resonance (MR)-guided radiation therapy (RT) is enhanc- ing head and neck cancer (HNC) treatment through superior soft tissue con- trast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)- driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT auto- segmentation performance. Participants tackled two HNC segmentation tasks: automatic delineation of primary gross tumor volume (GTVp) and gross metastatic regional lymph nodes (GTVn) on pre-RT (Task 1) and mid-RT (Task 2) T2- weighted scans. The challenge provided 150 HNC cases for training and 50 for final testing hosted on *** using a Docker submission framework. In total, 19 independent teams from across the world qualified by submitting both their algorithms and corresponding papers, resulting in 18 submissions for Task 1 and 15 submissions for Task 2. Evaluation using the mean aggregated Dice Simi- larity Coefficient showed top-performing AI methods achieved scores of 0.825 in Task 1 and 0.733 in Task 2. These results surpassed clinician interobserver vari- ability benchmarks, marking significant strides in automated tumor segmentation for MR-guided RT applications in HNC. © The Author(s) 2025.

关键词： Deep learning

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A cross-sectional study on gut microbiota and inflammation in patients with chronic kidney disease

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The American journal of the medical sciences 2025年

作者： Bruna R Paiva Júnia Schultz Fluvio Modolon Jessyca S de Brito Julie A Kemp Marcia Ribeiro Marcelo Ribeiro-Alves Lia S Nakao Drielly Vargas Beatriz G Baptista Larissa Fonseca Livia Alvarenga Intikhab Alam Roberto Incitti Alexandre Soares Rosado Ludmila F M F Cardozo Denise Mafra Graduate Program in Cardiovascular Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Bioscience Program Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia. Oceanographic Institute University of São Paulo São Paulo SP Brazil. Graduate Program in Biological Sciences - Physiology Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro RJ Brazil. Graduate Program in Nutrition Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. HIV/AIDS Clinical Research Center National Institute of Infectology Evandro Chagas (INI/Fiocruz) Rio de Janeiro RJ Brazil. Department of Basic Pathology Federal University of Paraná Curitiba PR Brazil. Graduate Program in Medical Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Graduate Program in Biological Sciences - Physiology Federal University of Rio de Janeiro (UFRJ) Rio de Janeiro RJ Brazil Computational Bioscience Research Center Biological and Environmental Science and Engineering (BESE) Division King Abdullah University of Science and Technology (KAUST) Thuwal Makkah 23955 Saudi Arabia. Graduate Program in Cardiovascular Sciences Fluminense Federal University (UFF) Niterói RJ Brazil Graduate Program in Nutrition Sciences Fluminense Federal University (UFF) Niterói RJ Brazil Graduate Program in Medical Sciences Fluminense Federal University (UFF) Niterói RJ Brazil. Electronic address: dmafra30@***.

BACKGROUND:This study investigated the inflammatory and gut microbiota profile in chronic kidney disease (CKD) patients undergoing hemodialysis (HD) and peritoneal dialysis (PD). METHODS:A total of 249 patients undergoing HD and 61 patients on PD participated in the study. The mRNA expressions of nuclear factor erythroid 2-related factor-2 (NRF2), nuclear factor-κappa B (NF-κB), mitochondrial transcription factor A (TFAM), peroxisome proliferator-activated receptor-gamma coactivator 1-alpha (PGC-1α) were evaluated in peripheral blood mononuclear cells (PBMCs) by quantitative real-time PCR. Malondialdehyde (MDA), interleukin 6 (IL-6), and routine biochemical parameters were also analyzed. The fecal DNA extraction was performed, and the V4 regions of the bacterial 16S ribosomal RNA gene were sequenced. Uremic toxins such as p-cresyl sulfate (p-CS), indoxyl sulfate (IS), and indole-3-acetic acid (IAA) plasma levels were determined by HPLC. RESULTS:MDA, IS, and p-CS levels were lower in PD than in HD patients. The mRNA expression of the transcription factors was not different between groups. Gut microbial α-diversity indices showed no significant difference between groups, but the β-diversity was different in PD patients. Members of the genera Meditarraneibacter, Roseburia, Agathobacter, Anaerobutyricum, Collinsella, Streptococcus, Clostridium, and Bacteroides, as well as the families Lachnospiraceae and Enterobacteriaceae, appear to be positively correlated with most dietary factors, particularly lipid and phosphorus intake. CONCLUSION:Our findings indicate that in patients with CKD on HD, there is increased plasma retention of uremic toxins and reduced gut microbial diversity compared to PD patients.

关键词： chronic kidney disease gut microbiota inflammation mitochondrial dysfunction oxidative stress

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BPS2025 - Biophysical approaches to study viral proteins at the cellular membranes

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Biophysical Journal 2025年第3期124卷 547a-547a页

作者： Elka R. Georgieva Md Majharul Islam Adeyemi Ogunbowale Saman Majeed Lan Dang Peter P. Borbat Olamide Ishola Elaheh Hadadianpour Arvin S. Delkhosh Juan C. Rueda Amador Steven Ludtke Department of Chemistry and Biochemistry Texas Tech University Lubbock TX USA Graduate Program in Quantitative and Computational Biosciences Graduate School of Biomedical Sciences Baylor College of Medicine Houston TX USA Department of Chemistry and Chemical Biology Cornell University Ithaca NY USA Department of Biochemistry and Molecular Pharmacology Baylor College of Medicine Houston TX USA

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