About

Diwakar Shukla is a professor in the Department of Chemical and Biomolecular Engineering at the University of Illinois at Urbana-Champaign. His research work is focused on understanding the complex molecular processes in chemical and biomolecular systems using computational and experimental techniques. He is also an affiliate faculty member in the Center for Biophysics and Quantitative Biology, Department of Plant Biology, Bioengineering, and Chemistry. Shukla's primary research area is computational bioengineering, specifically biomolecular modeling, molecular engineering, molecular modeling and simulations, and biophysics. He has a background in chemical engineering, having received his B.Tech and M.Tech degrees from the Indian Institute of Technology Bombay, and his M.S. and Ph.D. degrees from the Massachusetts Institute of Technology. His research career includes postdoctoral work at Stanford University in the lab of Prof. Vijay S. Pande. Throughout his career, Shukla has received numerous awards for his research and teaching, including early-career faculty awards from prominent scientific organizations, the Dean's Award for excellence in research, and teaching honors. His contributions include advancing understanding of molecular mechanisms in biological systems through computational modeling and simulations.

Research topics

• Biology
• Biochemistry
• Chemistry
• Genetics
• Pathology
• Medicine
• Immunology
• Chemical physics
• Internal medicine
• Organic chemistry
• Chemical engineering
• Bioinformatics
• Virology
• Computational biology
• Computational chemistry

Selected publications

• Solvent Environment Influences Molecular Conformation and Electron Transport in Peptides

  The Journal of Physical Chemistry Letters · 2026-05-17

  articleCorresponding

  Hierarchical structures play a key role in governing the electronic properties of peptides. Despite recent advances, establishing clear structure–property relationships that connect the solvent environment, molecular conformation, and electron transport at the single-molecule level remains challenging. Here, we use a combination of single-molecule experiments, molecular dynamics (MD) simulations, and machine learning (ML) analysis to understand how electron transport in peptides depends on solvent conditions for several different environments including water, 2,2,2-trifluoroethanol, acetonitrile, and glycerol. Our results reveal two distinct conductance populations for peptides in water or 2,2,2-trifluoroethanol: a high-conductance state associated with defined secondary structures (β turns or 310 helices) and a low-conductance state corresponding to extended primary structures. Peptides show a diminished high-conductance state in acetonitrile, which is known to weakly stabilize secondary structures and denature peptides. Interestingly, the high-conductance state is diminished in glycerol for tetrapeptides but not for pentapeptides. Unsupervised ML analysis using silhouette clustering and Gaussian mixture modeling suggests that solvent-dependent conductance behavior is mediated by peptide conformation. Complementary MD simulations, time-lagged independent component analysis of intramolecular hydrogen-bonding (H-bonding) distances, and Pearson correlation coefficients further reveal how solvent-peptide interactions and secondary structures govern electron transport pathways. Overall, our results show that the solvent environment significantly influences electron transport in peptides mediated by secondary structure and H-bonding interactions.

  PublisherDOI

• Multiple modes of cholesterol translocation in the human Smoothened receptor

  eLife · 2026-02-09

  articleOpen accessSenior author

  Smoothened (SMO), a member of the G Protein-Coupled Receptor superfamily, mediates Hedgehog signaling and is linked to cancer and birth defects. SMO responds to accessible cholesterol in the ciliary membrane, translocating it via a longitudinal tunnel to its extracellular domain. Reaching a complete mechanistic understanding of the cholesterol translocation process would help in the development of cancer therapies. Experimental data suggest two modes of translocation to support entry of cholesterol from outer and inner membrane leaflets, but the exact mechanism of translocation remains unclear. Using atomistic molecular dynamics simulations (∼2 millisecond simulations) and biochemical assays of SMO mutants, we assess the energetic feasibilities of the two modes. We show that the highest energetic barrier for cholesterol translocation from the outer leaflet is lower than that from the inner leaflet. Mutagenesis experiments and complementary simulations of SMO mutants validate the role of critical amino acid residues along the translocation pathways. Our data suggests that cholesterol can take either pathway to enter SMO, thus explaining experimental observations in the literature. Thus, our results illuminate the energetics and provide a first molecular description of cholesterol translocation in SMO.

  PublisherDOI

• Sequence constraints predispose Class D GPCRs to follow an atypical activation mechanism

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-14

  articleSenior author

  The biophysical principles underlying distinct conformational changes in proteins with similar topologies remain poorly understood. Class D G Protein-Coupled Receptors (GPCRs), fungal pheromone-sensing receptors essential for mating and survival, exhibit an atypical activation mechanism compared to other GPCR classes. Unlike Class A GPCRs, which activate through outward movement of TM6, Class D receptors undergo activation via outward displacement of TM7 coupled with inward movement of TM6. To investigate the origin of this atypical process, we employed state-specific generative AI sequence models to design protein sequences corresponding to unique active states, revealing that sequence constraints predispose Class D GPCRs toward this mechanism. We further explored the dynamic basis of activation through millisecond-scale atomistic simulations of STE2, a representative Class D GPCR and therapeutic target for fungal diseases. Using Maximum Entropy VAMPNets, an active learning based adaptive sampling strategy, we efficiently mapped the conformational free energy landscape of STE2. These simulations uncovered multiple intermediate states that have not yet been resolved experimentally and demonstrated that activation in these dimeric proteins involves fully decoupled monomers. Comparative simulations across Classes A, B, D, and F, totaling 4 milliseconds of all-atom molecular dynamics, revealed distinct patterns of electrostatic interactions. In Class D GPCRs, activation disrupts a dense TM7 interaction network while inward TM6 movement enhances electrostatic contacts. In contrast, Class A GPCRs such as CB1R display the opposite trend. Together, these findings show that sequence differences in TM6 and TM7 underlie the unique activation mechanism of STE2, offering new insights into GPCR conformational diversity.

  PublisherDOI

• How Minor Sequence Changes Enable Mechanistic Diversity in MFS Transporters? An Atomic-Level Rationale for Symport Emergence in NarU

  Journal of Chemical Information and Modeling · 2026-02-06

  articleSenior authorCorresponding

  Closely related membrane transporters can diverge sharply in their modes of transport despite minimal sequence differences, underscoring how minor structural features can alter the transport function. This divergence is exemplified in nitrate and nitrite transport across bacterial membranes, which supports anaerobic respiration and involves the major facilitator superfamily (MFS) transporters NarK and NarU. NarK operates as a nitrate/nitrite antiporter, whereas NarU’s mechanism remains unresolved, with evidence suggesting potential symport activity. Using extensive adaptive molecular dynamics simulations and Markov State Modeling, we mapped NarU’s conformational free-energy landscape and assessed how its behavior contrasts with mechanistic principles established for NarK. NarU follows a similar gating pathway but displays pronounced asymmetry favoring the outward-facing state and stabilizes an apo-occluded intermediate inaccessible to antiporters. This state arises from rotation of an arginine gating pair and a hinged glycine substitution that enhances gate flexibility. These sequence-dependent adaptations alter gating energetics and reprogram the scaffold to permit coupled cotransport. Our results show that the presence of a few strategic residue substitutions in the binding pocket and translocation pathway could alter the transport mechanism of transporters with high sequence and structural similarity.

  PublisherDOI

• BPS2026 – Mechanistic insights into glucose transport for PfHT1 from molecular dynamics and graph attention models

  Biophysical Journal · 2026-02-01

  articleSenior author
  PublisherDOI

• Data for: Sequence constraints predispose Class D GPCR STE2 to follow an atypical activation mechanism

  Open MIND · 2026-01-01

  datasetSenior author

  This dataset contains the .npy and .pkl files required to reproduce the plots in the study.

  DOI

• Learning physical interactions to compose biological large language models

  Communications Chemistry · 2026-01-07

  articleOpen accessSenior authorCorresponding

  Deep learning models have become fundamental tools in drug design. In particular, large language models trained on biochemical sequences learn feature vectors that guide drug discovery through virtual screening. However, such models do not capture the molecular interactions important for binding affinity and specificity. Therefore, there is a need to merge representations from distinct biological modalities to effectively represent molecular complexes. We present an overview of the methods to combine molecular representations and propose that future work should develop biochemical foundation models that jointly encode diverse molecular modalities. Specifically, learning to merge the representations from internal layers of domain specific biological language models could improve generalizability in the context of interaction prediction. We demonstrate that ‘composing’ biochemical language models performs similar or better than standard methods representing molecular interactions despite having significantly fewer features. We also discuss recent methods for interpreting and democratizing large language models that could aid the development of interaction aware foundation models for biology. Finally, we present a vision for future research that allows for predicting the evolution of molecular interactions across biophysical contexts. Deep learning models are pivotal in drug design, yet they often fail to capture crucial molecular interactions for binding affinity and specificity. Here, the authors present an overview of the methods to integrate molecular representations and propose that future work should develop biochemical foundation models that jointly encode diverse molecular modalities.

  PublisherOA PDFDOI

• De novo Folding Mechanisms of Lasso Peptides

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-04-01

  articleOpen accessSenior authorCorresponding

  Lasso peptides adopt a distinctive rotaxane conformation, yet the principles governing the folding of this kinetically trapped structure have remained elusive. Here, we integrated extensive molecular dynamics simulations and deep learning to elucidate the de novo folding mechanism of 20 lasso peptides lacking secondary post-translational modifications. We constructed Multi-Ensemble Markov Models for each lasso peptide and uncovered a universal uphill folding landscape with spontaneous folding probabilities consistently below 0.8%. Loop stability strongly correlated with folding propensity, and targeted experiments further validated that enhancing loop β-hairpin formation promotes folding of microcin J25, the well-studied lasso peptide extensively characterized as an in vitro model. Additionally, the substantial entropy cost opposed lasso peptide folding. Simulations mimicking enzymatic spatial confinement reduced this penalty and stabilize folding. Leveraging Variational AutoEncoder-based pathway clustering, we resolved distinct pathway channels and representative folding pathways. Together, these findings establish representative folding models and fundamental thermodynamic and kinetic principles for rational engineering of lasso peptides.

  PublisherOA PDFDOI

• Data for Dynamic Mechanism for Subtype Selectivity of Endocannabinoids

  Illinois Data Bank · 2026-01-01

  datasetOpen accessSenior author

  The dataset contains unbiased molecular dynamics (MD) trajectories in XTC format for anandamide binding in cannabinoid receptors, along with the files containing corresponding parameter and topology. All simulations employed the CHARMM36m force field for proteins, while endocannabinoids were parameterized using the CGenFF force field. Unbiased simulations were performed with OpenMM v7.7.

  PublisherDOI

• All roads lead to Rome: Emergence of Mechanistic Divergence Shapes Activation of Class B1 G-Protein Coupled Receptors

  bioRxiv (Cold Spring Harbor Laboratory) · 2026-01-05 · 1 citations

  articleOpen accessSenior authorCorresponding

  Class B1 GPCRs are crucial to maintaining homeostasis along a multitude of vital biochemical pathways. Understanding the activation mechanism of these proteins at both a family and clade-specific level is particularly relevant for designing multi-target agonists, as exemplified by recently designed dual-agonists for GLP-1R and GIPR, for treating obesity. Here, we use 6 milliseconds of unbiased all-atom MD simulations of GCGR, GLP1R, PAC1R, SCTR, PTH1R and CALCR from the four different clades of Class B1 GPCRs to establish the universal mechanism of their activation. We show that the activation of Class B1 GPCRs involves a clade-independent intermediate state characterized by the outward movement of helix 6. We use a combination of Markov state models and transition path theory to show that the activation of these proteins occurs at a millisecond timescale. We identify characteristic molecular locks that are conserved at a clade-level, showcasing the uniqueness among the activation mechanisms of these proteins. We show that these proteins show similar inactive and active states, but show unique activation mechanisms at a residue level. These sites can be targeted directly or allosterically to design therapeutics targeting a specific clade of proteins. Thus, this study provides an integrated atomistic view of the activation for Class B1 GPCRs from a mechanistic, thermodynamic and kinetic perspective.

  PublisherOA PDFDOI

Recent grants

• Elucidating sequence, structural and dynamic basis of the functional regulation of membrane proteins

  NIH · $1.8M · 2021–2026

• CAREER: Reinforcement Learning of the Free Energy Landscapes of Proteins

  NSF · $700k · 2018–2024

Frequent coauthors

• Balaji Selvam

  University of Illinois Urbana-Champaign

  47 shared

• Erik Procko

  Cyrus Biotechnology

  38 shared

• Matthew C. Chan

  Langley Research Center

  36 shared

• Chuankai Zhao

  Harbin Institute of Technology

  29 shared

• Soumajit Dutta

  University of Illinois Urbana-Champaign

  23 shared

• Jiming Chen

  Second Affiliated Hospital of Zhejiang University

  20 shared

• Vijay S. Pande

  19 shared

• Zahra Shamsi

  18 shared

Labs

• Diwakar Shukla LabPI

Education

• Ph.D., Bioengineering

  University of Illinois Urbana-Champaign

  2000

• M.S., Bioengineering

  University of Illinois Urbana-Champaign

  1996

• B.S., Bioengineering

  University of Illinois Urbana-Champaign

  1994

Awards & honors

• Dean's Award for Excellence in Research (2020, 2024)
• Stanley H. Pierce Award for Excellence in Mentoring (2024)
• Engineering Council Outstanding Advising Award (2016, 2024)
• Teaching Excellence Award from the School of Chemical Scienc…
• Engineering Council Outstanding Advising Award (2015, 2024)