Evidence map›Paper›PMID 35647916›Full record

ArticleActa crystallographica. Section D, Structural biology2022

Elucidation of protein function using computational docking and hotspot analysis by ClusPro and FTMap.

George Jones, Akhil Jindal, Usman Ghani, Sergei Kotelnikov, Megan Egbert, Nasser Hashemi, Sandor Vajda, Dzmitry Padhorny, Dima Kozakov

Abstract read
In one paragraph

Article in Acta crystallographica. Section D, Structural biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 70 papers.

0numbers the graph read from it
0cells of the map it votes in
70citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

70 citing papers in PubMed.

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  14. In Silico Study of Anti-CD40 DNA Aptamers as Vaccine Adjuvants for Chickens.International journal of molecular sciences · 2026
    Article
  15. Article
  16. A Scalable Framework for Comprehensive Typing of Polymorphic Immune Genes from Long-Read Data.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Article
  17. Article
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10 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

George JonesDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
Akhil JindalDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Usman GhaniDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Sergei KotelnikovDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
Megan EgbertDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Nasser HashemiDepartment of Systems Engineering, Boston University, Boston, Massachusetts, USA.
Sandor VajdaDepartment of Biomedical Engineering, Boston University, Boston, Massachusetts, USA.
Dzmitry PadhornyDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.
Dima KozakovDepartment of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY 11794, USA.

Funding

Analysis and Prediction of Molecular InteractionsR35GM118078 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI SANDOR VAJDA · 2016 to 2026
$6.5M
Solvation modeling for next-gen biomolecule simulationsRM1GM135136 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI COUTSIAS, EVANGELOS A., DILL, KEN A · 2020 to 2024
$6.0M
Simulation of Multi-Protein systemsR01GM140098 · NIGMS · STATE UNIVERSITY NEW YORK STONY BROOK · PI KOZAKOV, DMYTRO · 2021 to 2024
$1.4M
Optimizing and Learning Strategies for Protein Docking R01GM135930 · NIGMS · BOSTON UNIVERSITY (CHARLES RIVER CAMPUS) · PI VAKILI, PIROOZ · 2019 to 2021
$561k
National Science Foundation AF 1645512National Science Foundation DBI 1759277National Science Foundation DMS 2054251NIGMS NIH HHS R01 GM135930NIGMS NIH HHS R01 GM140098NIGMS NIH HHS R35 774 GM118078NIGMS NIH HHS R35 GM118078NIGMS NIH HHS RM1135136 775NIGMS NIH HHS RM1 GM135136
6 · The paper itself

Abstract

Starting with a crystal structure of a macromolecule, computational structural modeling can help to understand the associated biological processes, structure and function, as well as to reduce the number of further experiments required to characterize a given molecular entity. In the past decade, two classes of powerful automated tools for investigating the binding properties of proteins have been developed: the protein-protein docking program ClusPro and the FTMap and FTSite programs for protein hotspot identification. These methods have been widely used by the research community by means of publicly available online servers, and models built using these automated tools have been reported in a large number of publications. Importantly, additional experimental information can be leveraged to further improve the predictive power of these approaches. Here, an overview of the methods and their biological applications is provided together with a brief interpretation of the results.

Indexed as

ProteinsComputer SimulationMolecular Docking SimulationProtein ConformationProteinsClusProfast Fourier transformFTMaphotspotsmappingprotein docking

Identifiers

PMID35647916
PMCPMC9159284

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.