Folding project #15415, ENZYME-DYNAMICS research

Research section Research projects and references

Project #15415 overview

Project Summary AI Beta

This project relates to studying how enzymes change shape to interact with other molecules. By using computer simulations, researchers are looking at how these changes affect how enzymes bind to things like drugs. Understanding this could help develop new drugs and improve our understanding of how proteins work.

Automated summary; simplified and may not be fully accurate.

Project team

Manager(s): Adrija Dutta
Institution: UIUC

Work unit

Atoms: 81,948
Core: 0x24
Status: Public

Source material

Official Project Description

Protein function is closely linked to its dynamic structural behavior, particularly in regions involved in molecular recognition.

Using large-scale molecular dynamics simulations, we are studying intrinsic conformational variability across a diverse set of enzymes.

By analyzing binding pocket flexibility, structural rearrangements, and transient conformations, we aim to understand how active-site dynamics influence ligand binding.

These insights can support advances in drug discovery, enzyme engineering, and a deeper understanding of protein function.

Reading reference

Related Terms Glossary AI Beta

Automated high-level definitions; simplified and may not be fully accurate.

Protein

A large biomolecule made up of amino acids.

Scientific: PharmaceuticalBiotechnology / Structural Biology

Proteins are essential molecules found in all living organisms. They perform a wide range of functions, including catalyzing biochemical reactions, transporting molecules, providing structural support, and regulating cellular processes. Their complex three-dimensional structures enable them to interact with other molecules in specific ways.

Molecular dynamics

A computer simulation method used to study the motion of atoms and molecules over time.

Technical: PharmaceuticalBiotechnology / Computational Biology

Molecular dynamics simulations are used to understand how molecules move and interact. They can be applied to study a wide range of systems, including proteins, DNA, and drug-target complexes. By simulating the movement of atoms over time, researchers can gain insights into the mechanisms underlying biological processes.

Binding pocket

A specific region on a protein where a ligand can bind.

Scientific: BiotechPharmacology / Drug Discovery

The binding pocket is a crucial site on proteins that allows them to interact with other molecules. Ligands, such as drugs or substrates, fit into these pockets and trigger specific biological responses. Understanding the structure and function of binding pockets is essential for drug design and development.

Ligand

A molecule that binds to a receptor or protein.

Scientific: BiotechPharmacology / Drug Discovery

Ligands are molecules that interact with specific target sites on proteins. They can be natural substances, like hormones, or synthetic compounds, such as drugs. Ligand binding often triggers cellular responses and plays a key role in various biological processes.

Drug discovery

The process of identifying and developing new drugs.

Process: BiotechPharmacology / Research & Development

Drug discovery is a complex and multi-stage process that involves identifying potential drug candidates, testing their effectiveness and safety, and ultimately bringing them to market. It requires expertise in various fields, including chemistry, biology, and medicine.

Enzyme

A protein that catalyzes a biochemical reaction.

Scientific: PharmaceuticalBiotechnology / Metabolism

Enzymes are biological catalysts that accelerate chemical reactions within living organisms. They play essential roles in various metabolic processes, such as digestion, energy production, and DNA replication.

Active-site

The region on an enzyme where the substrate binds and the reaction takes place.

Scientific: PharmaceuticalBiochemistry / Enzymology

The active site is a specialized region on an enzyme where substrates bind and undergo chemical transformations. The shape and chemical properties of the active site are crucial for enzyme specificity and catalytic activity.

GPU PPD Averages

Rank Project	Model Name Folding@Home Identifier	Make Brand	GPU Model	PPD Average	Points WU Average	WUs Day Average	WU Time Average
1	GeForce RTX 5080 GB203 [GeForce RTX 5080]	Nvidia	GB203	23,568,758	34,714	678.94	0 ^hrs 2 ^mins
2	GeForce RTX 4080 AD103 [GeForce RTX 4080]	Nvidia	AD103	17,594,856	34,714	506.85	0 ^hrs 3 ^mins
3	GeForce RTX 3090 GA102 [GeForce RTX 3090]	Nvidia	GA102	8,803,202	248,691	35.40	0 ^hrs 41 ^mins
4	GeForce RTX 3070 Ti GA104 [GeForce RTX 3070 Ti]	Nvidia	GA104	6,591,935	34,714	189.89	0 ^hrs 8 ^mins
5	GeForce RTX 3080 GA102 [GeForce RTX 3080]	Nvidia	GA102	6,418,589	34,714	184.90	0 ^hrs 8 ^mins
6	GeForce RTX 3070 Lite Hash Rate GA104 [GeForce RTX 3070 Lite Hash Rate]	Nvidia	GA104	5,265,992	34,714	151.70	0 ^hrs 9 ^mins
7	Radeon RX 6950 XT Navi 21 [Radeon RX 6950 XT]	AMD	Navi 21	5,189,277	34,714	149.49	0 ^hrs 10 ^mins
8	Radeon RX 9070(XT) Navi 48 [Radeon RX 9070(XT)]	AMD	Navi 48	4,941,210	34,714	142.34	0 ^hrs 10 ^mins
9	Radeon RX 6800(XT)/6900XT Navi 21 [Radeon RX 6800(XT)/6900XT]	AMD	Navi 21	4,615,092	34,714	132.95	0 ^hrs 11 ^mins
10	Radeon RX 7900XT/XTX/GRE Navi 31 [Radeon RX 7900XT/XTX/GRE]	AMD	Navi 31	3,573,254	34,714	102.93	0 ^hrs 14 ^mins
11	GeForce RTX 3060 Ti GA104 [GeForce RTX 3060 Ti]	Nvidia	GA104	3,452,576	34,714	99.46	0 ^hrs 14 ^mins
12	GeForce RTX 3060 Ti Lite Hash Rate GA104 [GeForce RTX 3060 Ti Lite Hash Rate]	Nvidia	GA104	3,333,415	34,714	96.03	0 ^hrs 15 ^mins
13	Intel Arc B580 Graphics Battlemage G21 [Intel Arc B580 Graphics]	Intel	Battlemage G21	3,097,536	34,714	89.23	0 ^hrs 16 ^mins
14	GeForce GTX 1080 Ti GP102 [GeForce GTX 1080 Ti] 11380	Nvidia	GP102	2,710,464	34,714	78.08	0 ^hrs 18 ^mins
15	GeForce RTX 2060 Super TU106 [GeForce RTX 2060 Super]	Nvidia	TU106	2,606,865	34,714	75.10	0 ^hrs 19 ^mins
16	Radeon RX 6700(XT)/6800M Navi 22 XT-XL [Radeon RX 6700(XT)/6800M]	AMD	Navi 22 XT-XL	2,466,478	34,714	71.05	0 ^hrs 20 ^mins
17	GeForce RTX 2060 TU106 [Geforce RTX 2060]	Nvidia	TU106	2,003,611	136,815	14.64	1 ^hrs 38 ^mins
18	GeForce GTX 1080 GP104 [GeForce GTX 1080] 8873	Nvidia	GP104	1,482,736	138,849	10.68	2 ^hrs 15 ^mins
19	GeForce GTX 980 Ti GM200 [GeForce GTX 980 Ti] 5632	Nvidia	GM200	1,465,028	137,890	10.62	2 ^hrs 16 ^mins

20	GeForce GTX 1070 GP104 [GeForce GTX 1070] 6463	Nvidia	GP104	1,335,577	134,111	9.96	2 ^hrs 25 ^mins
21	GeForce GTX 1660 SUPER TU116 [GeForce GTX 1660 SUPER]	Nvidia	TU116	952,849	49,691	19.18	1 ^hrs 15 ^mins
22	GeForce GTX 970 GM204 [GeForce GTX 970] 3494	Nvidia	GM204	660,275	34,714	19.02	1 ^hrs 16 ^mins
23	R9 Fury X/NANO Fiji XT [R9 Fury X/NANO]	AMD	Fiji XT	457,500	34,714	13.18	1 ^hrs 49 ^mins
24	GeForce GTX 1050 Ti GP107 [GeForce GTX 1050 Ti] 2138	Nvidia	GP107	381,891	88,040	4.34	5 ^hrs 32 ^mins
25	Quadro M5000 GM204GL [Quadro M5000]	Nvidia	GM204GL	378,903	34,714	10.91	2 ^hrs 12 ^mins
26	Quadro P1000 GP107GL [Quadro P1000]	Nvidia	GP107GL	255,868	77,125	3.32	7 ^hrs 14 ^mins
27	Quadro K5200 GK110 [Quadro K5200]	Nvidia	GK110	224,989	34,714	6.48	3 ^hrs 42 ^mins

Research: ENZYME-DYNAMICS Folding Project #15415

Project Summary AI Beta

Project team

Work unit

Official Project Description

Hardware Performance for Project 15415

GPU PPD Averages

Research: ENZYME-DYNAMICS Folding Project #15415

Project Summary AI Beta

Project team

Work unit

Related projects

GPU PPD Averages