Paolo Marcatili

Molecular Biology · 2014–2022

A researcher in the Science corpus: 12 papers; trusts 13 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 141 citations into it; #13,823 of 17,773 by trust here, #7,041 by citations
• Computational Biology & Bioinformatics — 42 citations into it
• Immunology — 35 citations into it
• Radiology, Nuclear Medicine and Imaging — 34 citations into it; #2,361 of 3,265 by trust here, #1,169 by citations
• Materials Chemistry — 29 citations into it
• Spectroscopy — 12 citations into it

Whom Paolo Marcatili trusts overall

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

1. Morten Nielsen
2. Cyrus Chothia
3. Georgii G. Krivov
4. Yang Zhang
5. Aroop Sircar
6. Jian Ye
7. Brian D. Weitzner
8. Jeffrey Skolnick
9. Arthur M. Lesk
10. Roland L. Dunbrack

Who trusts Paolo Marcatili overall

1. Rosalba Lepore
2. Pier Paolo Olimpieri

Papers from Paolo Marcatili's perspective

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

1. The Protein Data Bank
2. Highly accurate protein structure prediction with AlphaFold
3. HHblits: lightning-fast iterative protein sequence searching by HMM-HMM alignment
4. Prediction of MHC class I binding peptides using profile motifs. (Human Immunology)
5. Antibody modeling assessment
6. Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations
7. The immune epitope database (IEDB) 3.0 (Nucleic Acids Res.)
8. Computational design of antibody-affinity improvement beyond in vivo maturation (Nature Biotechnology)
9. ElliPro: a new structure-based tool for the prediction of antibody epitopes (BMC Bioinformatics)
10. NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Their papers

• BepiPred-2.0: improving sequence-based B-cell epitope prediction using conformational epitopes (2017) — 1,496 citations
• Improved methods for predicting peptide binding affinity to MHC class II molecules (2018) — 725 citations
• Antibody modeling using the Prediction of ImmunoGlobulin Structure (PIGS) web server (2014) — 48 citations
• NetMHCpan 4.0: Improved peptide-MHC class I interaction predictions integrating eluted ligand and peptide binding affinity data (2017) — 1,083 citations
• NetSurfP-2.0: improved prediction of protein structural features by integrated deep learning (2018) — 550 citations
• ArrayPitope: Automated Analysis of Amino Acid Substitutions for Peptide Microarray-Based Antibody Epitope Mapping (2017) — 16 citations
• IEDB-AR: immune epitope database—analysis resource in 2019 (2019) — 357 citations
• DeepTMHMM predicts alpha and beta transmembrane proteins using deep neural networks (2022) — 989 citations
• NetSurfP-3.0: accurate and fast prediction of protein structural features by protein language models and deep learning (2022) — 295 citations
• Maximum-Entropy Models of Sequenced Immune Repertoires Predict Antigen-Antibody Affinity (2016) — 33 citations
• LYRA, a webserver for lymphocyte receptor structural modeling (2015) — 76 citations
• improved prediction of protein structural features by integrated deep learning