Petras J. Kundrotas

Molecular Biology · 2012–2021

A researcher in the Science corpus: 11 papers; trusts 38 researchers and is trusted by 16 researchers here, as inferred from the citation graph.

Subfields

Whom Petras J. Kundrotas trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

  1. Ilya A. Vakser
  2. Yang Zhang
  3. Marc F. Lensink
  4. John Moult
  5. Joël Janin
  6. Jeffrey Skolnick
  7. Patrick Aloy
  8. Shoshana J. Wodak
  9. Manfred J. Sippl
  10. Krzysztof Fidelis

Who trusts Petras J. Kundrotas overall

  1. Patrick Bryant
  2. Gabriele Pozzati
  3. Amar Singh
  4. András Szilágyi
  5. Ragul Gowthaman
  6. Tiziano Gallo Cassarino
  7. Jérôme Tubiana
  8. Marc F. Lensink
  9. Arne Elofsson
  10. Torsten Schwede

Papers from Petras J. Kundrotas's perspective

Ordered by how strongly the researchers Petras J. Kundrotas trusts endorse each paper. Same corpus, a different order for every researcher.

  1. TM-align: a protein structure alignment algorithm based on the TM-score
  2. Docking and scoring protein complexes: CAPRI 3rd Edition
  3. Optimization of affinity, specificity and function of designed influenza inhibitors using deep sequencing
  4. Prediction of homo- and hetero-protein complexes by protein docking and template-based modeling: a CASP-CAPRI experiment
  5. Calculation of conformational ensembles from potentials of mean force. An approach to the knowledge-based prediction of local structures in globular proteins.
  6. Robust, high-throughput solution structural analyses by small angle X-ray scattering (SAXS) (Nature Methods)
  7. Distance‐scaled, finite ideal‐gas reference state improves structure‐derived potentials of mean force for structure selection and stability prediction
  8. Molecular surface recognition: determination of geometric fit between proteins and their ligands by correlation techniques.
  9. Statistical potential for assessment and prediction of protein structures
  10. Faculty Opinions recommendation of Accurate prediction of protein structures and interactions using a three-track neural network.

Their papers