About

Eric Evans is a Professor of the Practice at MIT AeroAstro and the Security Studies Program. He holds a Small Satellite Collaborative academic degree in Electrical Engineering from The Ohio State University. Evans is a Fellow of IEEE and AIAA, and a member of the National Academy of Engineering. His professional roles include Director Emeritus and Fellow at MIT Lincoln Laboratory from 2006 to 2024. He has served on the Defense Science Board since 2009, including as vice chair from 2014 to 2020 and chair from 2020 to 2024. Additionally, he is a member of the Massport Security Advisory Council, an advisor to the U.S. Strategic Command Senior Advisory Group, and the chair of the board of directors for the National GEM Consortium. Evans has also been involved with the Los Alamos and Lawrence Livermore National Laboratory Nuclear Mission Committees. His research and professional focus encompass autonomous systems, decision-making, earth and space sciences, and aerospace innovation, contributing significantly to the fields of aerospace engineering and national security.

Research topics

• Computer Science
• Biology
• Computational biology
• Genetics
• Machine Learning
• Data Mining
• Artificial Intelligence
• Microbiology
• Bioinformatics
• Evolutionary biology

Selected publications

• Balanced Permeability Index: A Multiparameter Index for Improved In Vitro Permeability

  ACS Medicinal Chemistry Letters · 2024-03-19 · 12 citations

  articleOpen access

  The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp-mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity, and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

  PublisherOA PDFDOI

• Asymmetric Dirhodium-Catalyzed Modification of Immunomodulatory Imide Drugs and Their Biological Assessment

  ACS Medicinal Chemistry Letters · 2024-08-23 · 8 citations

  articleOpen access

  Cereblon (CRBN) has been successfully co-opted to affect the targeted degradation of "undruggable" proteins with immunomodulatory imide drugs (IMiDs). IMiDs act as molecule glues that facilitate ternary complex formation between CRBN and a target protein, leading to ubiquitination and proteasomal degradation. Subtle structural modifications often cause profound and sometimes unpredictable changes in the degradation selectivity. Herein, we successfully utilize enantioselective cyclopropanation and cyclopropenation on intact glutarimides to enable the preparation of stereochemically and regiochemically matched molecular pairs for structure-activity relationship (SAR) analysis across several classical CRBN neosubstrates. The resulting glutarimide analogs were found to reside in unique chemical space when compared to other IMiDs in the public domain. SAR studies revealed that, in addition to the more precedented impacts of regiochemistry, stereochemical modifications far from the glutarimide can lead to divergent neosubstrate selectivity. These findings emphasize the importance of enabling enantioselective methods for glutarimide-containing compounds to tune the degradation selectivity.

  PublisherDOI

• Balanced Permeability Index: a multi-parameter index for improved in-vitro permeability

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-12-04 · 2 citations

  preprintOpen access

  ABSTRACT The optimization of passive permeability is a key objective for orally available small molecule drug candidates. For drugs targeting the central nervous system (CNS), minimizing P-gp mediated efflux is an additional important target for optimization. The physicochemical properties most strongly associated with high passive permeability and lower P-gp efflux are size, polarity and lipophilicity. In this study, a new metric called the Balanced Permeability Index (BPI) was developed that combines these three properties. The BPI was found to be more effective than any single property in classifying molecules based on their permeability and efflux across a diverse range of chemicals and assays. The BPI can also be used to guide optimization in non-traditional small molecule modalities, such as protein degraders, which often lie outside of traditional small molecule space. BPI is easy to understand, allowing researchers to make decisions about which properties to prioritize during the drug development process.

  PublisherOA PDFDOI

• High-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

  Science · 2022 · 276 citations

  • Biology
  • Computational biology
  • Genetics

  . Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

  PublisherOA PDFDOI

• Conformational Landscape Reduction of a Dynamic 29-Residue Peptide by a Perfluoroaromatic Small Molecule

  ChemRxiv · 2020-07-22

  preprintOpen access1st authorCorresponding

  Conformationally dynamic peptides and proteins display both important biochemical properties and present a challenge for computational modeling. Characterizing the accessible structural landscape represents one route to understand their function with molecular level detail. We characterize a self-labeling 29-residue peptide, MP01-Gen4, that undergoes structural alterations in the presence of a perfluoroaromatic reaction partner. Replica exchange molecular dynamics (REMD) shows MP01 to access a broad set of states, that microsecond-long explicit solvent simulations only minimally sample. REMD and structural network analysis find an altered and reduced conformational landscape when MP01 interacts non-covalently or is covalently attached to the perfluoroaromatic small molecule. Residues throughout the peptide, notably at the C-terminus, interact with the small molecule in conformational state-dependent manners. The results help explain and generate hypotheses for experimental observations including the importance of flexibility and the role of the N- and C-terminal regions, both of which are distant from the active cysteine. The simulations highlight the importance of substantial sampling in minimally stabilized, conformationally dynamic systems and supplies a case study for small molecule-mediated, peptide conformational changes.<br>

  PublisherDOI

• Conformational Landscape Reduction of a Dynamic 29-Residue Peptide by a Perfluoroaromatic Small Molecule

  ChemRxiv · 2020-07-21

  preprintOpen access1st authorCorresponding

  Conformationally dynamic peptides and proteins display both important biochemical properties and present a challenge for computational modeling. Characterizing the accessible structural landscape represents one route to understand their function with molecular level detail. We characterize a self-labeling 29-residue peptide, MP01-Gen4, that undergoes structural alterations in the presence of a perfluoroaromatic reaction partner. Replica exchange molecular dynamics (REMD) shows MP01 to access a broad set of states, that microsecond-long explicit solvent simulations only minimally sample. REMD and structural network analysis find an altered and reduced conformational landscape when MP01 interacts non-covalently or is covalently attached to the perfluoroaromatic small molecule. Residues throughout the peptide, notably at the C-terminus, interact with the small molecule in conformational state-dependent manners. The results help explain and generate hypotheses for experimental observations including the importance of flexibility and the role of the N- and C-terminal regions, both of which are distant from the active cysteine. The simulations highlight the importance of substantial sampling in minimally stabilized, conformationally dynamic systems and supplies a case study for small molecule-mediated, peptide conformational changes.

  PublisherOA PDFDOI

• Predicting human health from biofluid-based metabolomics using machine learning

  Zenodo (CERN European Organization for Nuclear Research) · 2020-06-08

  datasetOpen access1st authorCorresponding

  Processed input data sets as well as output files and trained health state predictive models for human diseases using metabolomics of biofluids. These data are associated with the following manuscript: 'Predicting human health from biofluid-based metabolomics using machine learning' https://doi.org/10.1101/2020.01.29.20019471

  PublisherDOI

• Longitudinal wastewater sampling in buildings reveals temporal dynamics of metabolites

  PLoS Computational Biology · 2020-06-29 · 4 citations

  articleOpen access1st author

  Direct sampling of building wastewater has the potential to enable "precision public health" observations and interventions. Temporal sampling offers additional dynamic information that can be used to increase the informational content of individual metabolic "features", but few studies have focused on high-resolution sampling. Here, we sampled three spatially close buildings, revealing individual metabolomics features, retention time (rt) and mass-to-charge ratio (mz) pairs, that often possess similar stationary statistical properties, as expected from aggregate sampling. However, the temporal profiles of features-providing orthogonal information to physicochemical properties-illustrate that many possess different feature temporal dynamics (fTDs) across buildings, with large and unpredictable single day deviations from the mean. Internal to a building, numerous and seemingly unrelated features, with mz and rt differences up to hundreds of Daltons and seconds, display highly correlated fTDs, suggesting non-obvious feature relationships. Data-driven building classification achieves high sensitivity and specificity, and extracts building-identifying features found to possess unique dynamics. Analysis of fTDs from many short-duration samples allows for tailored community monitoring with applicability in public health studies.

  PublisherOA PDFDOI

• <i>Microbe-seq</i> : high-throughput, single-microbe genomics with strain resolution, applied to a human gut microbiome

  bioRxiv (Cold Spring Harbor Laboratory) · 2020-12-14 · 6 citations

  preprintOpen access

  Abstract We present Microbe-seq , a high-throughput single-microbe method that yields strain-resolved genomes from complex microbial communities. We encapsulate individual microbes into droplets with microfluidics and liberate their DNA, which we amplify, tag with droplet-specific barcodes, and sequence. We use Microbe-seq to explore the human gut microbiome; we collect stool samples from a single individual, sequence over 20,000 microbes, and reconstruct nearly-complete genomes of almost 100 bacterial species, including several with multiple subspecies strains. We use these genomes to probe genomic signatures of microbial interactions: we reconstruct the horizontal gene transfer (HGT) network within the individual and observe far greater exchange within the same bacterial phylum than between different phyla. We probe bacteria-virus interactions; unexpectedly, we identify a significant in vivo association between crAssphage, an abundant bacteriophage, and a single strain of Bacteroides vulgatus. Microbe-seq contributes high-throughput culture-free capabilities to investigate genomic blueprints of complex microbial communities with single-microbe resolution.

  PublisherOA PDFDOI

• Predicting human health from biofluid-based metabolomics using machine learning

  medRxiv · 2020-02-03 · 1 citations

  preprintOpen access1st author

  Abstract Biofluid-based metabolomics enables the profiling of thousands of molecules and has the potential to provide highly accurate, minimally invasive diagnostics for a range of health conditions. However, typical metabolomics studies focus on only a few statistically significant features. We study the applicability of machine learning for health state-prediction across 35 human mass spectrometry-based metabolomics studies. Models trained on all features outperform those using only significant features and frequently provide high predictive performance across nine health states, despite disparate experimental conditions and disease contexts. Combining data from different experimental settings (e.g. sample type, instrument, chromatography) within a study minimally alters predictive performance, suggesting information overlap between different methods. Using only non-significant features, we still often obtain high predictive performance. To facilitate further advances, we provide all data online. This work highlights the applicability of biofluid-based metabolomics with data-driven analysis for health state diagnostics.

  PublisherOA PDFDOI

Frequent coauthors

• Eric J. Alm

  Massachusetts Institute of Technology

  10 shared

• Bradley L. Pentelute

  Massachusetts Institute of Technology

  7 shared

• Chengzhen L. Dai

  Institute for Systems Biology

  5 shared

• Michael Oberst

  4 shared

• Yehang Yin

  Zhejiang University of Science and Technology

  4 shared

• Isaac Rockafellow

  Massachusetts Institute of Technology

  4 shared

• Wenshan Zheng

  4 shared

• Claire Duvallet

  4 shared

Awards & honors

• Secretary of Defense Distinguished Public Service Award, 202…
• M. Barry Carlton Award, IEEE Aerospace and Electronics Syste…
• Fellow, IEEE
• Fellow, AIAA
• Member, National Academy of Engineering