{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/117647"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/117647","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Study of proteins and nucleic acids using molecular dynamics and machine learning techniques","abstract":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_html":"Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","abstract_has_math":false,"creators":["Trifan, Anda"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"Ph.D.","degree_level":"Dissertation","degree_discipline":"Biophysics & Quant Biology","degree_department":null,"school":null,"contributors":["Tajkhorshid, Emad","Ramanathan, Arvind","Pogorelov, Taras","Sligar, Steven"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-12","date_published":"2022-12","updated_at":"2026-07-22T22:24:56Z","subjects":["Molecular Dynamics"],"languages":["en","eng"],"rights":["Copyright 2022 Anda Trifan"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/117647","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Tajkhorshid, Emad","Ramanathan, Arvind","Pogorelov, Taras","Sligar, Steven"]},{"key":"dc:creator","label":"Author","values":["Trifan, Anda"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-12","2022-11-21"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Biophysics & Quant Biology"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Ph.D."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Molecular Dynamics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2022 Anda Trifan"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/117647"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Anda Trifan, accepted the attached license on 2022-11-12 at 11:09.","The student, Anda Trifan, submitted this Dissertation for approval on 2022-11-12 at 11:20.","This Dissertation was approved for publication on 2022-11-21 at 14:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18568 on 2023-04-12 at 08:10:50","With recent advances in computational power, molecular dynamics (MD) simulations have become an indispensable tool in studying biophysical phenomena. MD provides not only atomistic details of a system, but also temporal information. This dissertation presents several projects studied with MD simulations to understand underlying biophysical problems. We first characterize the lipid-protein interactions of a small GTPase, K-Ras, a signaling protein whose specific mutations cause it to remain in an active state persistently, driving oncogenic activity in cells. Using highly mobile membrane mimetic (HMMM) membranes with enhanced lipid diffusion, we show the globular domain (G-domain), adapts a preferred conformation on the surface of the membrane due to tethering by the hypervariable region (HVR). The HVR significantly restrains the conformations adapted by K-Ras, playing an important role in its orientation and subsequent availability to bind to downstream effectors. The mechanism of the drug remdesivir (RDV), a recently FDA-approved drug to treat COVID-19, is also studied in this dissertation. RDV is a nucleotide analog which incorporates into RNA and stalls its translocation in the RNA-dependent RNA polymerase (RdRP). We simulated the translocation using an array of non-equilibrium methods and show atomistic details of both the translocation event of RDV as well as control simulations with adenosine. Interaction energies characterize the interactions of the two nucleotides with their surroundings to help understand their distinct mechanisms. In order to achieve longer timescales, we developed an innovative framework to combine MD simulations with fluctuating finite element analysis (FFEA) to study the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC). Here we bridge the gap between the two resolutions and use ML/AI methods to orchestrate the workflow and elucidate large scale conformational changes that the RTC undergoes. AI methods have been combined with MD simulations to also study the spike protein of SARS-CoV-2. We have investigated the dynamics of the spike within different environments, including its binding to the ACE2 human receptor as well as within the full virion. We have also studied the role of the spike glycans which form a shield on the surface of the spike. We show how AI accelerates probing the conformational landscape leading to decreased time to observe experimentally determined structures using computation. Finally, we present a novel workflow accelerating drug discovery efforts to address the COVID-19 pandemic by combining MD simulations, ML techniques, and free energy calculations. Taking advantage of supercomputing power, we are able to achieve extremely high throughput and identify inhibitors for important SARS-CoV-2 target proteins such as the papain-like protease, PLPro."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Study of proteins and nucleic acids using molecular dynamics and machine learning techniques"]}]}],"canonical_facts":{"dc:contributor":["Tajkhorshid, Emad","Ramanathan, Arvind","Pogorelov, Taras","Sligar, Steven"],"dc:creator":["Trifan, Anda"],"dc:date":["2022-12","2022-11-21"],"dc:description":["Submission published under a 24 month embargo labeled 'U of I Access', the embargo will last until 2024-12-01","The student, Anda Trifan, accepted the attached license on 2022-11-12 at 11:09.","The student, Anda Trifan, submitted this Dissertation for approval on 2022-11-12 at 11:20.","This Dissertation was approved for publication on 2022-11-21 at 14:30.","DSpace SAF Submission Ingestion Package generated from Vireo submission #18568 on 2023-04-12 at 08:10:50","With recent advances in computational power, molecular dynamics (MD) simulations have become an indispensable tool in studying biophysical phenomena. MD provides not only atomistic details of a system, but also temporal information. This dissertation presents several projects studied with MD simulations to understand underlying biophysical problems. We first characterize the lipid-protein interactions of a small GTPase, K-Ras, a signaling protein whose specific mutations cause it to remain in an active state persistently, driving oncogenic activity in cells. Using highly mobile membrane mimetic (HMMM) membranes with enhanced lipid diffusion, we show the globular domain (G-domain), adapts a preferred conformation on the surface of the membrane due to tethering by the hypervariable region (HVR). The HVR significantly restrains the conformations adapted by K-Ras, playing an important role in its orientation and subsequent availability to bind to downstream effectors. The mechanism of the drug remdesivir (RDV), a recently FDA-approved drug to treat COVID-19, is also studied in this dissertation. RDV is a nucleotide analog which incorporates into RNA and stalls its translocation in the RNA-dependent RNA polymerase (RdRP). We simulated the translocation using an array of non-equilibrium methods and show atomistic details of both the translocation event of RDV as well as control simulations with adenosine. Interaction energies characterize the interactions of the two nucleotides with their surroundings to help understand their distinct mechanisms. In order to achieve longer timescales, we developed an innovative framework to combine MD simulations with fluctuating finite element analysis (FFEA) to study the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC). Here we bridge the gap between the two resolutions and use ML/AI methods to orchestrate the workflow and elucidate large scale conformational changes that the RTC undergoes. AI methods have been combined with MD simulations to also study the spike protein of SARS-CoV-2. We have investigated the dynamics of the spike within different environments, including its binding to the ACE2 human receptor as well as within the full virion. We have also studied the role of the spike glycans which form a shield on the surface of the spike. We show how AI accelerates probing the conformational landscape leading to decreased time to observe experimentally determined structures using computation. Finally, we present a novel workflow accelerating drug discovery efforts to address the COVID-19 pandemic by combining MD simulations, ML techniques, and free energy calculations. Taking advantage of supercomputing power, we are able to achieve extremely high throughput and identify inhibitors for important SARS-CoV-2 target proteins such as the papain-like protease, PLPro."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/117647"],"dc:language":["en","eng"],"dc:rights":["Copyright 2022 Anda Trifan"],"dc:subject":["Molecular Dynamics"],"dc:title":["Study of proteins and nucleic acids using molecular dynamics and machine learning techniques"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Biophysics & Quant Biology"],"thesis:degree_level":["Dissertation"],"thesis:degree_name":["Ph.D."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:56Z"}